A23 PHYSICIAN FACTORS ASSOCIATED WITH ADENOMA DETECTION AT COLONOSCOPY
Bibliographic record
Abstract
A physician’s adenoma detection rate (ADR) is inversely associated with post-colonoscopy colorectal cancer (CRC) incidence and mortality. In BC, the Colon Screening Program (BCCSP) was implemented in Nov. 2013, with the goal of standardizing CRC screening. Colonoscopy quality assurance initiatives within the BCCSP include monitoring physicians’ ADR, completion rates, complications as well as Direct Observation of Procedural Skills (DOPS), a validated formative assessment of colonoscopy skills. To evaluate whether physician characteristics are associated with the discovery of an adenoma in patients undergoing colonoscopy to investigate a positive fecal immunochemistry test (FIT). Data is collected prospectively from all participants in the BCCSP including age, gender, FIT value, physician performing the colonoscopy, colonoscopy findings and pathology. Physician variables are publically available through the BC College of Physicians and Surgeons’. All colonoscopies performed for a positive FIT through the BCCSP between 11/13-12/17 were included. The total number of colonoscopies performed by each physician was not available, only those performed in the BCCSP. A mixed effects logistics regression was used for data analysis. 87,542 colonoscopies performed by 263 physicians on patients between the ages of 50–74 years were included. Of the physicians, 76% were men. 65% were surgeons, 31% were gastroenterologists (GI), 3% internal medicine, and 1% family physicians. 71% had completed DOPS. The majority (39%) graduated after 2000 and 86% graduated from North American (NA) medical schools. The median annual volume of BCCSP colonoscopies per physician was 72 (10th, 90th percentile: 3, 173). Sub-specialty training in gastroenterology, a higher volume of colonoscopies within the program and more recent traininig were significantly associated with the detection of an adenoma at colonoscopy on multivariable regression analysis while controlling for patient age, gender and FIT value. None
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".