Assessment of a Colonoscopy Triage Sheet for Use in a Province-Wide Population-Based Colorectal Screening Program
Bibliographic record
Abstract
Background and Aims. A colonoscopy triage sheet (CTS) integrating 6 hierarchical scheduling priorities based on indications for screening, surveillance, or symptoms was designed for colonoscopy referral. We compared CTS priority ratings by referring physicians and endoscopists, assessing yields. Methods. Retrospective study of consecutive patients. Data were collected on demographics, CTS and endoscopist priority ratings, and endoscopic findings. Weighted kappa values measured interrater agreement on priority assignment. Predictors of agreement and lesions were identified using multivariable analysis. Results. Among 1230 patients (60.3 years, 52.5% female), clinically significant lesions included tumors (1.1%), polyps per patient ≥ 10 mm (7.6%), and ileocolitis (4.6%). Moderate agreement was found between referring physician and endoscopist on all 6 priorities (weighted kappa 0.55 (0.51; 0.59)). P4 and P5 ratings predicted increased agreement (range of OR for P4: 2.47-4.57; P5: 1.58-2.93). Predictors of clinically significant findings were male gender (OR 1.44, 1.03-2.03) and P1/P2 priorities that were significantly superior to P3 (OR = 2.14; 1.04-4.43), P4 (OR = 2.90; 1.35-6.23), and P5 (OR = 4.30; 2.08-8.88). Conclusion. Priority-assignment agreement is moderate and highest for less urgent ratings. Predictors of clinically significant findings validate the hierarchal priority scheme. Broader validation and physician education are needed.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".