Rates of polypectomy in screening and non-screening colonoscopies classified by patient and endoscopist reports
Bibliographic record
Abstract
Background: Polypectomy rate may be related to indicators of quality assurance for screening colonoscopy. However, it is difficult to identify screening colonoscopies in provincial health databases.Objective: To estimate polypectomy rates for screening colonoscopy according to patient and endoscopist reported indications and to compare them to published quality indicators.Methods: A retrospective cohort study was conducted of staff endoscopists at 7 Montreal hospitals and their patients aged 50-75 who underwent colonoscopy. Consecutive patients were interviewed by a research assistant in the waiting room prior to colonoscopy. Patient reported indication was defined in 4 ways: 1) perceived screening (routine screening, family history, age); 2) perceived non-screening (follow-up); 3) medical history indicating non-screening; 4) combination of the 3 indications. Endoscopist indication was derived from a questionnaire completed immediately after colonoscopy. Polypectomy status was obtained from Quebec provincial physician billing records. Polypectomy rates were computed, while accounting for physician and hospital level clustering, using all 4 patient indications, endoscopist indication, and the agreement between patient and endoscopist indications. Polypectomy rates were adjusted for the accuracy of provincial databases. Results: 2143 patients (mean age=61, 50% female) were included. Adjusted polypectomy rates ranged between 22.6-26.2% for screening colonoscopy and between 27.1-30.8% for non-screening. Polypectomy rates for screening colonoscopy were 16.3-19.6 % in women and 29.1-34.2% in men. These rates fall below the published benchmarks for polypectomy rates of 30% in women and 40% in men. Conclusion: Polypectomy rates calculated from the different screening definitions were similar and fall below quality benchmarks.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".