Endoscopist specialty is associated with colonoscopy quality
Bibliographic record
Abstract
BACKGROUND: Some studies have shown that endoscopist specialty is associated with colorectal cancers missed by colonoscopy. We sought to examine the relationship between endoscopist specialty and polypectomy rate, a colonoscopy quality indicator. Polypectomy rate is defined as the proportion of colonoscopies that result in the removal of one or more polyps. METHODS: A cross-sectional study was conducted of endoscopists and their patients from 7 Montreal and 2 Calgary endoscopy clinics. Eligible patients were aged 50-75 and covered by provincial health insurance. A patient questionnaire assessed family history of colorectal cancer, history of large bowel conditions and symptoms, and previous colonoscopy. The outcome, polypectomy status, was obtained from provincial health administrative databases. For each city, Bayesian hierarchical logistic regression was used to estimate the odds ratio for polypectomy comparing surgeons to gastroenterologists. Model covariates included patient age, sex, family history of colorectal cancer, colonoscopy indication, and previous colonoscopy. RESULTS: In total, 2,113 and 538 colonoscopies were included from Montreal and Calgary, respectively. Colonoscopies were performed by 38 gastroenterologists and 6 surgeons in Montreal, and by 31 gastroenterologists and 5 surgeons in Calgary. The adjusted odds ratios comparing surgeons to gastroenterologists were 0.48 (95% CI: 0.32-0.71) in Montreal and 0.73 (95% CI: 0.43-1.21) in Calgary. CONCLUSIONS: An association between endoscopist specialty and polypectomy was observed in both cities after adjusting for patient-level covariates. Results from Montreal suggest that surgeons are half as likely as gastroenterologists to remove polyps, while those from Calgary were associated with a wide, non-significant Bayesian credible interval. However, residual confounding from patient-level variables is possible, and further investigation is required.
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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 | 0.010 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".