A scoping review of unexpected weight loss and cancer: risk, guidelines, and recommendations for follow-up in primary care
Bibliographic record
Abstract
BACKGROUND: Cancer diagnoses often begin with consultations with GPs, but the non-specific nature of symptoms can lead to delayed diagnosis. Unexpected weight loss (UWL) is a common non-specific symptom linked to undiagnosed cancer, yet guidelines for its diagnostic assessment in general practice lack consistency. AIM: To synthesise evidence on the association between UWL and cancer diagnosis, and to review clinical guidelines and recommendations for assessing patients with UWL. DESIGN & SETTING: Systematic search and analysis of studies conducted in primary care. METHOD: Four databases were searched for peer-reviewed literature from 2012 to 2023. Two reviewers conducted all the steps. A narrative review was conducted detailing the evidence for UWL as a risk factor for undiagnosed cancer, existing clinical guidance, and recommended diagnostic approach. RESULTS: We included 25 studies involving 916 092 patients; 92% provided strong evidence of an association between UWL and undiagnosed cancer. The National Institute for Health Care and Excellence (NICE) Cancer Guideline in the UK was frequently cited. General suggestions encompassed regular weight monitoring, family history, risk factor evaluation, additional signs and symptoms, and a comprehensive physical examination. Commonly recommended pathology tests included C-reactive protein (CRP), complete blood count, alkaline phosphatase, and thyroid-stimulating hormone. Immunochemical faecal occult blood test, abdominal ultrasound, and chest X-ray were also prevalent. One large cohort study provided age, sex, and differential diagnosis-specific recommendations. CONCLUSION: This evidence review informs recommendations for investigating patients with UWL and will contribute to a computer decision support tool implementation in primary care, enhance UWL assessment, and potentially facilitate earlier cancer diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".