Different Screening Definitions have Little Impact on Polypectomy Rate Estimates
Bibliographic record
Abstract
BACKGROUND: Polypectomy rate is a surrogate quality indicator for screening colonoscopy. Various methods for identifying screening colonoscopies have been used and it is unclear how different definitions affect the estimated polypectomy rate. OBJECTIVE: To estimate polypectomy rates and how they vary according to the definition of a screening colonoscopy, using patient- and endoscopist-reported indications. METHODS: A cross-sectional analysis of endoscopists and their patients 50 to 75 years of age who underwent colonoscopy was conducted. Based on questionnaire responses, four patient indications were derived: perceived screening; perceived nonscreening; medical history indicating nonscreening; and combination of the three indications. Endoscopist indication was derived from a questionnaire completed immediately after colonoscopy. Polypectomy status was obtained from provincial physician billing records. Polypectomy rates were computed, while accounting for physician and hospital level clustering, using all four patient indications, endoscopist indication, and the agreement between patient and endoscopist indications. The effect of indications on polypectomy rate was estimated adjusting for age, sex and family history of colorectal cancer. RESULTS: A total of 2134 patients and 45 endoscopists were included. The proportion of colonoscopies classified as screening according to the nine indications ranged from 32.2% to 70.9%. Polypectomy rates ranged between 22.6% and 26.2% for screening colonoscopy, and between 27.1% and 30.8% for nonscreening colonoscopy. Adjusted ORs for indication ranged between 0.74 and 0.94. DISCUSSION: Although the proportion of colonoscopies identified as screening varied considerably among the indications, the estimated polypectomy rates were similar. CONCLUSION: The findings suggest that the way screening is defined does not greatly affect the estimates of polypectomy rate.
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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 | 0.367 | 0.710 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".