Screening polypectomy rates below quality benchmarks:A prospective study
Bibliographic record
Abstract
AIM:To estimate and compare sex-specific screening polypectomy rates to quality benchmarks of 40%in men and 30%in women.METHODS:A prospective cohort study was undertaken of patients aged 50-75,scheduled for colonoscopy,and covered by the Quebec universal health insurance plan.Endoscopist and patient questionnaires were used to obtain screening and non-screening colonoscopy indications.Patient self-report was used to obtain history of gastrointestinal conditions/symptoms and prior colonoscopy.Sex-specific polypectomy rates(PRs)and95%CI were calculated using Bayesian hierarchical logistic regression.RESULTS:In total,45 endoscopists and 2134(mean age=61,50%female)of their patients participated.According to patients,screening PRs in males and females were 32.4%(95%CI:23.8-41.8)and19.4%(95%CI:13.1-25.4),respectively.According to endoscopists,screening PRs in males and females were 30.2%(95%CI:27.0-41.9)and 16.6%(95%CI:16.3-28.6),respectively.Sex-specific PRs did not meet quality benchmarks at all ages except for:males aged65-69(patient screening indication),and males aged70-74(endoscopist screening indication).For all patients aged 50-54,none of the CI included the quality benchmarks.CONCLUSION:Most sex-specific screening PRs in Quebec were below quality benchmarks;PRs were especially low for all 50-54 year olds.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".