Diagnostic utility of alarm features for colorectal cancer: systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Colorectal cancer is the second most common cause of cancer death in Europe and North America. Alarm features are used to prioritize access to urgent investigation, but there is little information concerning their utility in the diagnosis of colorectal cancer. METHODS: A systematic review and meta-analysis of the published literature was carried out to assess the diagnostic accuracy of alarm features in predicting colorectal cancer. Primary or secondary care-based studies in unselected cohorts of adult patients with lower gastrointestinal symptoms were identified by searching MEDLINE, EMBASE and CINAHL (up to October 2007). The main outcome measures were accuracy of alarm features or statistical models in predicting the presence of colorectal cancer after investigation. Data were pooled to estimate sensitivity, specificity, and positive and negative likelihood ratios. The quality of the included studies was assessed according to predefined criteria. RESULTS: Of 11 169 studies identified, 205 were retrieved for evaluation. Fifteen studies were eligible for inclusion, evaluating 19 443 patients, with a pooled prevalence of colorectal carcinoma of 6% (95% CI 5% to 8%). Pooled sensitivity of alarm features was poor (5% to 64%) but specificity was >95% for dark red rectal bleeding and abdominal mass, suggesting that the presence of either rules the diagnosis of colorectal cancer in. Statistical models had a sensitivity of 90%, but poor specificity. CONCLUSIONS: Most alarm features had poor sensitivity and specificity for the diagnosis of colorectal carcinoma, whilst statistical models performed better in terms of sensitivity. Future studies should examine the utility of dark red rectal bleeding and abdominal mass, and concentrate on maximising specificity when validating statistical models.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it