Poor predictive value of lower gastrointestinal alarm features in the diagnosis of colorectal cancer in 1981 patients in secondary care
Bibliographic record
Abstract
BACKGROUND: Clinicians are advised to refer patients with lower gastrointestinal (GI) alarm features for urgent colonoscopy to exclude colorectal cancer (CRC). However, the utility of alarm features is debated. AIM: To assess whether performance of alarm features is improved by using a symptom frequency threshold to trigger referral, or by combining them into composite variables, including minimum age thresholds, as recommended by the National Institute for Health and Care Excellence (NICE). METHODS: We collected data prospectively from 1981 consecutive adults with lower GI symptoms. Assessors were blinded to symptom status. The reference standard to define CRC was histopathological confirmation of adenocarcinoma in biopsy specimens from a malignant-looking colorectal lesion. Controls were patients without CRC. Sensitivity, specificity, positive predictive values (PPVs) and negative predictive values were calculated for individual alarm features, as well as combinations of these. RESULTS: In identifying 47 (2.4%) patients with CRC, individual alarm features had sensitivities ranging from 11.1% (family history of CRC) to 66.0% (loose stools), and specificities from 30.5% (loose stools) to 75.6% (family history of CRC). Using higher symptom frequency thresholds improved specificity, but to the detriment of sensitivity. NICE referral criteria also had higher specificities and lower sensitivity, with PPVs above 4.8%. More than 80% of those with CRC met at least one of the NICE referral criteria. CONCLUSIONS: Using higher symptom frequency thresholds for alarm features improved specificity, but sensitivity was low. NICE referral criteria had PPVs above 4.8%, but sensitivities ranged from 2.2% to 32.6%, meaning many cancers would be missed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".