An Electronic Clinical Decision-Making Tool for Patients with Suspected Colorectal Cancer—Preliminary Evaluation in Patients Presenting with Rectal Bleeding
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: electronic clinical decision-making application was designed to assist physicians with evaluation of patients with suspected colorectal cancer (CRC). The physician completes an interactive checklist of evidence-based clinical parameters, and a recommended referral urgency is generated based on the post-test probability of CRC. This study aimed toward validation of the tool in symptomatic patients presenting with rectal bleeding. METHODS: tool was applied retrospectively to all patients who initially presented with rectal bleeding, to determine its sensitivity for detecting CRC in this population. A generated recommendation of 'immediate referral' (referral ≤24 hours, expected endoscopy ≤2 weeks) or 'urgent referral' (expected consultation and endoscopy ≤4 and ≤8 weeks) was considered a positive test result. An a priori sensitivity of 90% was deemed adequate, based on test characteristics of the tool's individual clinical criteria. RESULTS: The tool was applied to 281 patients. A total of 69 (24.6%) and 211 (75.1%) patients met criteria for immediate and urgent referral, respectively. The remaining patient (0.4%) met criteria for 'possible priority referral', while none met criteria for 'no specific action recommended'. This resulted in a calculated sensitivity of 99.6% (95% confidence interval 98.0 to 99.9%). CONCLUSIONS: tool is sensitive in the prediction of CRC in patients presenting with rectal bleeding. A prospective cohort study is being designed to allow for acquisition of comprehensive test performance characteristics and full validation of the instrument.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".