OBJECTIVES: To quantify and compare the rate of weight regain after cessation of weight management medications (WMMs) in adults with overweight or obesity. DESIGN: Systematic review and meta-analysis. STUDY SELECTION: Trial registries and databases (Medline, Embase, PsycINFO, CINAHL, Cochrane, Web of Science, and trial registries) were searched from inception until February 2025 for randomised controlled trials (RCTs), non-randomised trials, and observational studies that included WMM (\u22658 weeks) with follow-up for \u22654 weeks after cessation of treatment in adults with overweight or obesity. Comparators were any non-drug weight loss intervention or placebo. DATA EXTRACTION AND SYNTHESIS: The review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Two independent reviewers screened titles, extracted data, and assessed the risk of bias using the Cochrane Risk of Bias 2 tool for RCTs and the ROBINS-I tool for non-randomised trials. Data were analysed using mixed effect, meta-regression, and time-to-event models. Weight regain after cessation of WMM was compared with that reported after cessation of behavioural weight management programmes (BWMPs). MAIN OUTCOME MEASURES: The primary outcome was rate of weight regain from end of treatment, with associated changes in cardiometabolic markers as a secondary outcome. RESULTS: Of the 9288 titles screened, 37 studies (63 intervention arms, 9341 participants) were included. Average treatment duration was 39 (range 11-176) weeks, with average follow-up of 32 (4-104) weeks. The average monthly rate of weight regain was 0.4 kg (95% confidence interval (CI) 0.3 to 0.5) (mixed model 0.3 kg (0.2 to 0.4) monthly v control in RCTs). All cardiometabolic markers were projected to return to baseline within 1.4 years after the cessation of WMM. Weight regain was faster after WMM than after BWMP (by 0.3 kg (0.22 to 0.34) monthly), independent of initial weight loss. Estimates and precision were robust in sensitivity analyses. CONCLUSIONS: This review found that cessation of WMM is followed by rapid weight regain and reversal of beneficial effects on cardiometabolic markers. Regain after WMM was faster than after BWMP. These findings suggest caution in short term use of these drugs without a more comprehensive approach to weight management. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42024532069.
\n \n\n \n \nBackground: Incorrectly placed endotracheal tubes (ETTs) can lead to serious clinical harm. Studies have demonstrated the potential for artificial intelligence (AI)-led algorithms to detect ETT placement on chest X-Ray (CXR) images, however their effect on clinician accuracy remains unexplored. This study measured the impact of an AI-assisted ETT detection algorithm on the ability of clinical staff to correctly identify ETT misplacement on CXR images. Methods: Four hundred CXRs of intubated adult patients were retrospectively sourced from the John Radcliffe Hospital (Oxford) and two other UK NHS hospitals. Images were de-identified and selected from a range of clinical settings, including the intensive care unit (ICU) and emergency department (ED). Each image was independently reported by a panel of thoracic radiologists, whose consensus classification of ETT placement (correct, too low [distal], or too high [proximal]) served as the reference standard for the study. Correct ETT position was defined as the tip located 3\u20137 cm above the carina, in line with established guidelines. Eighteen clinical readers of varying seniority from six clinical specialties were recruited across four NHS hospitals. Readers viewed the dataset using an online platform and recorded a blinded classification of ETT position for each image. After a four-week washout period, this was repeated with assistance from an AI-assisted image interpretation tool. Reader accuracy, reported confidence, and timings were measured during each study phase. Results: 14,400 image interpretations were undertaken. Pooled accuracy for tube placement classification improved from 73.6 to 77.4% (p = 0.002). Accuracy for identification of critically misplaced tubes increased from 79.3 to 89.0% (p = 0.001). Reader confidence improved with AI assistance, with no change in mean interpretation time at 36 s per image. Conclusion: Use of assistive AI technology improved accuracy and confidence in interpreting ETT placement on CXR, especially for identification of critically misplaced tubes. AI assistance may potentially provide a useful adjunct to support clinicians in identifying misplaced ETTs on CXR.
\n \n\n \n \nOBJECTIVE: To characterise the use of the prostate specific antigen (PSA) test in primary care in England. DESIGN: Population based open cohort study.England. PARTICIPANTS: 10\u2009235\u2009805 male patients older than 18 years and registered at 1442 general practices that contributed to the Clinical Practice Research Datalink between 2000 and 2018. Data were linked to the National Cancer Registry, Hospital Episode Statistics, and Office for National Statistics. MAIN OUTCOME MEASURES: Population based temporal trends and annual percentage changes were analysed using age standardised PSA testing rates. Mixed effects negative binomial regression models investigated individual patient rate ratios of PSA testing. Linear mixed effects models examined factors associated with an individual patient's length of PSA retesting intervals. All results were analysed by region, deprivation, age, ethnicity, family history of prostate cancer, symptom presentation, and PSA value. RESULTS: 1\u2009521\u2009116 patients had at least one PSA test, resulting in 3\u2009835\u2009440 PSA tests overall. 48.4% (735\u2009750) of these patients had multiple tests and 72.8% (535\u2009990) of them never presented with a PSA value above the age specific referral threshold. The median retesting interval overall was 12.6 months (interquartile range 6.2-27.5). Testing rates varied by region, deprivation, ethnicity, family history, age, PSA value, and symptoms. Once tested, patients had shorter retesting intervals if they were older, were of an ethnicity other than white, had a family history of prostate cancer, or had previously raised PSA levels. Despite considerable variation in testing rates by region and deprivation, the length of retesting intervals was similar across these groups. CONCLUSIONS: PSA testing before a diagnosis of prostate cancer in primary care in England varied. Among patients who underwent multiple tests, many were tested more frequently than recommended, raising concerns about overtesting. PSA retesting is occurring in patients without recorded symptoms and in those with low PSA values. To ensure maximum benefit to patients while reducing the risk of overtesting, research is urgently needed to determine appropriate evidence based PSA retesting intervals.
\n \n\n \n \nObjectives: To summarise the recommendations for prostate-specific antigen (PSA) retesting intervals and to evaluate the evidence cited by each guideline by conducting a systematic review of clinical practice guidelines. Methods: We searched PubMed and the Turning Research into Practice (TRIP) database for guidelines written in English and developed or updated in 2013\u20132024. Guideline quality assessment was performed using the AGREE II tool. We narratively synthesised results. Results: Eleven guidelines were included. Ten (91%) recommended PSA retesting intervals of approximately 2 to 4 years. A total of 37 studies were referenced as evidence for the recommended intervals across the 11 guidelines. Five of these studies (14%) had the objective of determining PSA retesting intervals. Fourteen studies (38%) analysed single PSA test results. Five guideline recommendations partially aligned with the evidence referenced and five did not align. Conclusions: Generally, for asymptomatic patients aged \u226550 years with PSA levels between 1 and 3 ng/mL, most guidance recommended a retesting interval of 2\u20134 years, with the possibility to extend the interval to 4\u201310 years for patients with a PSA value <1 ng/mL. Until research generates direct evidence for PSA retesting intervals for both asymptomatic and symptomatic patients, clinicians and patients engaging in shared decision-making should be aware that current guidelines lack direct evidence for recommended PSA retesting intervals.
\n \n\n \n \nBackground Meningiomas are among the most common brain tumours in the United Kingdom and incidence is increasing. Up to 90% of meningiomas are Grade 1 (non-malignant) and have high survival. Brain tumour survivors are at risk of psychological and neurological late effects but risks in persons with non-malignant meningioma are not well characterized. Methods We used UK Biobank, a cohort of approximately 500\u2005000 adults recruited ages 40-69 during 2006-2010. Non-malignant meningioma patients were identified through linked cancer registry data. Follow-up for 10 outcomes was based on linkage to Hospital Episode Statistics. Standardized incidence ratios (SIRs) compared risks in meningioma patients to rates from UK Biobank overall, adjusted for age, sex, and calendar time. Results Four hundred and sixty-seven individuals were diagnosed with non-malignant meningioma after joining UK Biobank (77% female). Median age at diagnosis was 65 and median follow-up was 5 years. Persons with meningioma had significantly increased risks of 9 of 10 sequelae studied. The lowest SIR was for stroke (1.3, 95% confidence interval [CI] 0.6-2.9) and the highest SIRs were for epilepsy (18.9, 95%CI: 13.3-26.9) and visual disturbances (6.6, 95%CI: 3.3-13.1). SIRs for depression, anxiety, headache, fatigue, hearing loss, limb weakness, and cognitive issues (in ascending order) ranged from 2.3 (95%CI: 1.6-3.4) to 4.4 (95%CI: 2.8-6.9). Conclusions By providing preliminary evidence of excess risk of a range of long-term sequelae, this research can provide insight into future risks and validate survivor experience for people diagnosed with non-malignant meningioma and build momentum for future research using larger population-based databases and primary care records.
\n \n\n \n \nObjectives: Endometrial cancer (EC) incidence has been rising globally, while mortality has remained stable, particularly in Nordic countries. This study aimed to explore the role of transvaginal ultrasound (TVUS) use and overdiagnosis in these trends. Methods: We analysed crude and age-adjusted EC incidence and mortality rates by stage and age group using data from the Norwegian Cancer and Causes of Death Registries (1970-2019). Annual percentage changes (APC) in early-stage and late-stage cancers were estimated using linear regression. Trends were assessed with the Mann-Kendall test and Theil-Sen estimator. Spearman's correlation evaluated the relationship between regional TVUS billing rates and EC incidence. Results: From 1970 to 2019, age-standardised EC incidence in Norway increased by 79% (16.7-30.0 per 100 000), with a five-yearly growth rate of 7.47% (p=0.020). Mortality remained stable until 2000-2004, with a non-significant drop from 5.0 to 2.75 per 100 000 by 2019 (Sen's slope: -0.20, p=0.15). In postmenopausal women, early-stage incidence rose by 73% (32.1-55.5 per 100 000) and late-stage diagnoses increased by 108% (7.0-14.6 per 100 000). In premenopausal women, early-stage incidence declined by 24% (3.3-2.5 per 100 000), while late-stage diagnoses rose by 189% (0.23-0.66 per 100 000). TVUS use rose from 7.6% to 8.3% annually (2006-2019). The APC in postmenopausal women was similar across stages, with the largest increase in those aged 70+ years. Evidence for TVUS driving early-stage diagnoses was not strong, although a weak correlation between the two was observed in postmenopausal women (r=0.35, p<0.05). Conclusions: Overdiagnosis and stage migration may explain the rising EC incidence in postmenopausal women. In premenopausal women, overdiagnosis is less likely. TVUS is unlikely to be the main driver of the trends. Further research is needed to clarify the interplay of factors affecting EC trends globally.
\n \n\n \n \nBackground: Childhood, teenage and young adult (CTYA, 0\u201324 years) cancers are rare and diverse, making timely diagnosis challenging. We aim to explore symptoms and symptom combinations associated with a subsequent cancer diagnosis and to establish their timeframe. Methods: Using the QResearch Database, we carried out a matched nested case-control study. Associations between pre-specified symptoms encountered in primary care and a subsequent diagnosis of any cancer were explored using conditional logistic regression. Median diagnostic intervals were used to split symptoms into \u201clate\u201d and \u201cearly\u201d timeframes to identify relevant early symptoms. Results: 3186 cases and 50,576 controls were identified from a cohort of 3,424,771 CTYA. We identified 12 novel associations, of which hemiparesis [OR 90.9 (95%CI 24.7-335.1), PPV = 1.6%], testicular swelling [OR 186.7 (95%CI 86.1-404.8), PPV = 2.4%] and organomegaly [OR 221.6 (95%CI 28.3-1735.9), PPV = 5.4%] had significant positive predictive values (PPV). Limb pain, a known marker of serious illness in children, was a recurrent early symptom across cancer subtypes. Similar clinical presentations were observed across childhood and TYA cancers. Discussion: Using the largest cohort to date, we provide novel information on the time-varying predictive utility of symptoms in the diagnosis of CTYA cancers. Our findings will help to raise clinical and public awareness of symptoms, stratify those at higher-risk and ultimately aid earlier diagnosis.
\n \n\n \n \nBackground Artificial intelligence (AI)-assisted image interpretation is a fast-developing area of clinical innovation. Most research to date has focused on the performance of AI-assisted algorithms in comparison with that of radiologists rather than evaluating the algorithms\u2019 impact on the clinicians who often undertake initial image interpretation in routine clinical practice. This study assessed the impact of AI-assisted image interpretation on the diagnostic performance of frontline acute care clinicians for the detection of pneumothoraces (PTX). Methods A multicentre blinded multi-case multi-reader study was conducted between October 2021 and January 2022. The online study recruited 18 clinician readers from six different clinical specialties, with differing levels of seniority, across four English hospitals. The study included 395 plain CXR images, 189 positive for PTX and 206 negative. The reference standard was the consensus opinion of two thoracic radiologists with a third acting as arbitrator. General Electric Healthcare Critical Care Suite (GEHC CCS) PTX algorithm was applied to the final dataset. Readers individually interpreted the dataset without AI assistance, recording the presence or absence of a PTX and a confidence rating. Following a\u2019washout\u2019 period, this process was repeated including the AI output. Results Analysis of the performance of the algorithm for detecting or ruling out a PTX revealed an overall AUROC of 0.939. Overall reader sensitivity increased by 11.4% (95% CI 4.8, 18.0, p=0.002) from 66.8% (95% CI 57.3, 76.2) unaided to 78.1% aided (95% CI 72.2, 84.0, p=0.002), specificity 93.9% (95% CI 90.9, 97.0) without AI to 95.8% (95% CI 93.7, 97.9, p=0.247). The junior reader subgroup showed the largest improvement at 21.7% (95% CI 10.9, 32.6), increasing from 56.0% (95% CI 37.7, 74.3) to 77.7% (95% CI 65.8, 89.7, p<0.01). Conclusion The study indicates that AI-assisted image interpretation significantly enhances the diagnostic accuracy of clinicians in detecting PTX, particularly benefiting less experienced practitioners. While overall interpretation time remained unchanged, the use of AI improved diagnostic confidence and sensitivity, especially among junior clinicians. These findings underscore the potential of AI to support less skilled clinicians in acute care settings.
\n \n\n \n \nIntroduction Missed fractures are the most frequent diagnostic error attributed to clinicians in UK emergency departments and a significant cause of patient morbidity. Recently, advances in computer vision have led to artificial intelligence (AI)-enhanced model developments, which can support clinicians in the detection of fractures. Previous research has shown these models to have promising effects on diagnostic performance, but their impact on the diagnostic accuracy of clinicians in the National Health Service (NHS) setting has not yet been fully evaluated. Methods and analysis A dataset of 500 plain radiographs derived from Oxford University Hospitals (OUH) NHS Foundation Trust will be collated to include all bones except the skull, facial bones and cervical spine. The dataset will be split evenly between radiographs showing one or more fractures and those without. The reference ground truth for each image will be established through independent review by two senior musculoskeletal radiologists. A third senior radiologist will resolve disagreements between two primary radiologists. The dataset will be analysed by a commercially available AI tool, BoneView (Gleamer, Paris, France), and its accuracy for detecting fractures will be determined with reference to the ground truth diagnosis. We will undertake a multiple case multiple reader study in which clinicians interpret all images without AI support, then repeat the process with access to AI algorithm output following a 4-week washout. 18 clinicians will be recruited as readers from four hospitals in England, from six distinct clinical groups, each with three levels of seniority (early-stage, mid-stage and later-stage career). Changes in the accuracy, confidence and speed of reporting will be compared with and without AI support. Readers will use a secure web-based DICOM (Digital Imaging and Communications in Medicine) viewer (www.raiqc.com), allowing radiograph viewing and abnormality identification. Pooled analyses will be reported for overall reader performance as well as for subgroups including clinical role, level of seniority, pathological finding and difficulty of image. Ethics and dissemination The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved on 13 December 2022). The use of anonymised retrospective radiographs has been authorised by OUH NHS Foundation Trust. The results will be presented at relevant conferences and published in a peer-reviewed journal.
\n \n\n \n \nNeuropeptide Y (NPY) and related peptides have been proposed as promising biomarkers for the diagnosis of prostate cancer by previous immunoassays and immunohistochemical studies. In this study, we evaluated the additional value of NPY and related peptides compared with prostate-specific antigen (PSA). We performed a comprehensive analysis of NPY, its precursors, and metabolite concentrations in both plasma and tissue samples from 181 patients using a highly specific liquid chromatography tandem mass spectrometry method. Compared with PSA, NPY and related peptides (NPYs) were less accurate at diagnosing significant prostate cancer. Combinations of NPYs in a stepwise approach did not improve a model that would be beneficial for patients. NPY may be beneficial for patients presenting with a PSA concentration in the gray area between 4 and 9 ng/ml, but the strength of this conclusion is limited. Thus, the use of NPYs as standalone or in combination with other variables, such as PSA, prostate volume, or age, to improve the diagnosis is not supported by our study. Patient summary: This study evaluated neuropeptide Y (NPY) of the family of endogenous peptides as a new biomarker to diagnose prostate cancer. We found that NPY in a patient's blood was not more helpful at diagnosing prostate cancer than the standard prostate-specific antigen blood test. Further research is needed to explore the potential of NPY and related peptides in specific subgroups of patients.
\n \n\n \n \nObjectives: The aim of this systematic review was to summarize national and international guidelines that made recommendations for monitoring patients diagnosed with low-risk cancer. It appraised the quality of guidelines and determined whether the guidelines adequately identified patients for monitoring, specified which tests to use, defined monitoring intervals, and stated triggers for further intervention. It then assessed the evidence to support each recommendation. Study Design and Setting: Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses, we searched PubMed and Turning Research into Practice databases for national and international guidelines' that were written in English and developed or updated between 2012 and 2023. Quality of individual guidelines was assessed using the AGREE II tool. Results: Across the 41 published guidelines, 48 different recommendations were identified: 15 (31%) for prostate cancer, 11 (23%) for renal cancer, 6 (12.5%) for thyroid cancer, and 10 (21%) for blood cancer. The remaining 6 (12.5%) were for brain, gastrointestinal, oral cavity, bone and pheochromocytoma and paraganglioma cancer. When combining all guidelines, 48 (100%) stated which patients qualify for monitoring, 31 (65%) specified which tests to use, 25 (52%) provided recommendations for surveillance intervals, and 23 (48%) outlined triggers to initiate intervention. Across all cancer sites, there was a strong positive trend with higher levels of evidence being associated with an increased likelihood of a recommendation being specific (P = 0.001) and the evidence for intervals was based on expert opinion or other guidance. Conclusion: With the exception of prostate cancer, the evidence base for monitoring low-risk cancer is weak and consequently recommendations in clinical guidelines are inconsistent. There is a lack of direct evidence to support monitoring recommendations in the literature making guideline developers reliant on expert opinion, alternative guidelines, or indirect or nonspecific evidence.
\n \n\n \n \nIntroduction A non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investigation requested in the emergency department. Advances in computer vision have led to development of several artificial intelligence (AI) tools to detect abnormalities on NCCTH. These tools are intended to provide clinical decision support for clinicians, rather than stand-alone diagnostic devices. However, validation studies mostly compare AI performance against radiologists, and there is relative paucity of evidence on the impact of AI assistance on other healthcare staff who review NCCTH in their daily clinical practice. Methods and analysis A retrospective data set of 150 NCCTH will be compiled, to include 60 control cases and 90 cases with intracranial haemorrhage, hypodensities suggestive of infarct, midline shift, mass effect or skull fracture. The intracranial haemorrhage cases will be subclassified into extradural, subdural, subarachnoid, intraparenchymal and intraventricular. 30 readers will be recruited across four National Health Service (NHS) trusts including 10 general radiologists, 15 emergency medicine clinicians and 5 CT radiographers of varying experience. Readers will interpret each scan first without, then with, the assistance of the qER EU 2.0 AI tool, with an intervening 2-week washout period. Using a panel of neuroradiologists as ground truth, the stand-alone performance of qER will be assessed, and its impact on the readers\u2019 performance will be analysed as change in accuracy (area under the curve), median review time per scan and self-reported diagnostic confidence. Subgroup analyses will be performed by reader professional group, reader seniority, pathological finding, and neuroradiologist-rated difficulty. Ethics and dissemination The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved 13 December 2022). The use of anonymised retrospective NCCTH has been authorised by Oxford University Hospitals. The results will be presented at relevant conferences and published in a peer-reviewed journal.
\n \n\n \n \nImaging tests are commonly used as an initial or early investigation for patients presenting with suspected acute gastrointestinal bleeding (AGIB). However, controversy remains regarding which of two frequently used modalities, computed tomography angiography (CTA) or technetium-99m labelled red blood cell scintigraphy (RBCS), is most accurate. This systematic review and meta-analysis was performed to compare the accuracy of CTA and RBCS for the detection and localization of AGIB. Five electronic databases were searched with additional manual searching of reference lists of relevant publications identified during the search. Two reviewers independently performed screening, data extraction and methodological assessments. Where appropriate, the bivariate model was used for meta-analysis of sensitivities and specificities for the detection of bleeding and Freeman-Tukey double-arcsine transformation used for meta-analysis of proportions of correctly localized bleeding sites. Forty-four unique primary studies were included: twenty-two investigating CTA, seventeen investigating RBCS and five investigating both modalities. Meta-analysis produced similar pooled sensitivities; 0.83 (95% CI 0.74-0.90) and 0.84 (0.68-0.92) for CTA and RBCS respectively. Pooled specificity for CTA was higher than RBCS; 0.90 (0.72-0.97) and 0.84 (0.71-0.91) respectively. However, differences were not statistically significant. CTA was superior to RBCS in correctly localizing bleeding; pooled proportions of 1.00 (0.98-1.00) and 0.90 (0.83-0.96) respectively (statistically significant difference, P < 0.001). There is no evidence that CTA and RBCS have different diagnostic performance with respect to the detection of AGIB. However, CTA is superior to RBCS in terms of correctly localising the bleeding site, supporting usage of CTA over RBCS as the first line imaging investigation.
\n \n\n \n \nThe NHS Long Term Plan for cancer aims to increase early-stage diagnoses from 50% to 75% and to have 55,000 more people each year survive their cancer for at least 5 years following diagnosis. The targets measures are flawed and could be met without improving outcomes that really matter to patients. The proportion of early-stage diagnoses could increase, while the number of patients presenting at a late-stage remains the same. More patients could survive their cancer for longer, but lead time and overdiagnosis bias make it impossible to know whether anyone had their life prolonged. The target measures should switch from biased case-based measures to unbiased population-based measures that reflect the key objectives in cancer care: reducing late-stage incidence and mortality.
\n \n\n \n \nIMPORTANCE Lung cancer, the US's leading cause of cancer death, is often diagnosed following presentation to health care settings with symptoms, and many patients present with late stage disease. OBJECTIVE To investigate the association between weight loss and subsequent diagnosis of incident lung cancer in an ambulatory care population and to assess whether recorded weight change had higher odds of lung cancer diagnosis than objective measurements. DESIGN, SETTING, AND PARTICIPANTS This case-control study included patients visiting a US academic medical center between January 1, 2012, and December 31, 2019. Data were derived from US ambulatory care electronic health records from the University of Washington Medical Center linked to the local Surveillance, Epidemiology, and End Results cancer registry. Cases were identified from patients who had a primary lung cancer diagnosis between 2012 and 2019; controls were matched on age, sex, smoking status, and presenting to the same type of ambulatory clinic as cases. Data were analyzed from March 2022 through January 2023. EXPOSURE Continuous and categorical weight change were assessed. MAIN OUTCOMES AND MEASURES Odds ratios estimating the likelihood of a diagnosis of lung cancer were calculated using univariable and multivariable conditional logistic regression. RESULTS A total of 625 patients aged 40 years or older with a first primary lung cancer diagnosis and 4606 matched controls were included (1915 [36.6%] ages 60 to 69 years; 418 [8.0%] Asian, 389 [7.4%] Black, 4092 [78.2%] White). In unadjusted analyses, participants with weight loss of 1% to 3% (odds ratio [OR], 1.12; 95% CI, 0.88-1.41), 3% to 5% (OR, 1.36; 95% CI, 0.99-1.88), or 5% to 10% (OR, 1.23; 95% CI, 0.82-1.85) over a 2-year period did not have statistically significantly increased risk of lung cancer diagnosis compared with those who maintained a steady weight. However, participants with weight loss of 10% to 50% had more than twice the odds of a lung cancer diagnosis (OR, 2.27; 95% CI, 1.27-4.05). Most categories of weight loss showed significant associations with an increased risk of lung cancer diagnosis for at least 6 months prior to diagnosis. Patients who had weight loss both recorded in clinicians\u2019 notes and measured had higher odds of lung cancer compared with patients who had only recorded (OR, 1.26; odds; 95% CI, 1.04-1.52) or measured (OR, 8.53; 95% CI, 6.99-10.40) weight loss. CONCLUSIONS AND RELEVANCE In this case-control study, weight loss in the prior 6 months was associated with incident lung cancer diagnosis and was present whether weight loss was recorded as a symptom by the clinician or based on changes in routinely measured weight, demonstrating a potential opportunity for early diagnosis. The association between measured and recorded weight loss by clinicians presents novel results for the US.
\n \n\n \n \nIntroduction Primary cam morphology is highly prevalent in many athlete populations, causing debilitating hip osteoarthritis in some. Existing research is mired in confusion partly because stakeholders have not agreed on key primary cam morphology elements or a prioritised research agenda. We aimed to inform a more rigorous, inclusive and evidence-based approach to research on primary cam morphology and its natural history by working towards agreement on a set of research priorities for conditions affecting the young person's hip. Methods An international expert panel - the Young Athlete's Hip Research (YAHiR) Collaborative - rated research priority statements through an online two-round Delphi exercise and met online to explore areas of tension and dissent. Panellists ranked the prioritised research statements according to the Essential National Health Research (ENHR) ranking strategy. Reporting of results followed REPRISE (REporting guideline for PRIority SEtting of health). Results A diverse Delphi panel (n=65, Delphi rounds 1 and 2; three ENHR strategy surveys: n=49; n=44; n=42) from 18 countries representing six stakeholder groups, prioritised and ranked 18 of 38 research priority statements. The prioritised statements outlined seven research domains: (1) best practice physiotherapy, (2) rehabilitation progression and return to sport, (3) exercise intervention and load management, (4) primary cam morphology prognosis and aetiology, (5) femoroacetabular impingement syndrome prognosis and aetiology, (6) diagnostic criteria, and (7) screening. The panel recommended areas of tension and dissent for the research community to focus on immediately. Conclusion While informing more rigorous, inclusive and evidence-based research, this consensus is a roadmap for researchers, policy-makers and funders to implement research dedicated to reducing the cost and burden of hip disease related to primary cam morphology.
\n \n\n \n \nIntroduction Primary cam morphology is a mostly benign bony prominence that develops at the femoral head-neck junction of the hip, but it is highly prevalent in many athlete populations. In the small proportion of athletes for whom it is not benign, the resulting hip osteoarthritis can be debilitating. Clinicians, athletes, patients and researchers do not yet agree on important primary cam morphology elements. We aimed to ascertain and improve the level of agreement on primary cam morphology definitions, terminology, taxonomy and imaging outcome measures. Methods To collect and aggregate informed opinions, an expert panel - the Young Athlete's Hip Research Collaborative - rated primary cam morphology definition, terminology, taxonomy and imaging outcome statements through an online Delphi exercise followed by an online meeting to explore areas of tension and dissent. Reporting followed Conducting and REporting DElphi Studies. Results A diverse and inclusive Delphi panel (n=65 for rounds 1 and 2, representing 18 countries; 6 stakeholder groups; 40% women) agreed on 35 of 47 statements in 4 domains, while surfacing areas of tension and dissent. This Delphi panel agreed on four key issues essential to moving research and clinical care forward around primary cam morphology. They agreed on: (1) definition, confirming its conceptual attributes (tissue type, size, location, shape and ownership); (2) terminology - use morphology' and not terms with a negative connotation like lesion', abnormality' or deformity'; (3) taxonomy, distinguishing between primary and secondary cam morphology, and (4) imaging outcomes, a continuous bone/cartilage alpha angle on radial femoral head-neck MRI for primary cam morphology aetiology research. Conclusion This consensus provides athletes, patients, clinicians and researchers with a strong foundation to guide more precise communication, better clinical decision-making and higher value research about primary cam morphology and its natural history.
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