Should We Measure Adenoma Detection Rate for Gastroenterology Fellows in Training?
Bibliographic record
Abstract
BACKGROUND: Adenoma detection rate (ADR) is a proven quality metric for colonoscopy. The value of ADR for the evaluation of gastroenterology fellows is not well established. The aim of this study is to calculate and evaluate the utility of ADR as a measure of competency for gastroenterology fellows. METHODS: Colonoscopies for the purposes of screening and surveillance, on which gastroenterology fellows participated at the Richard L. Roudebush VAMC (one of the primary training sites at Indiana University), during a 9-month period, were included. ADR, cecal intubation rate, and indirect withdrawal time were measured. These metrics were compared between the levels of training. RESULTS: A total of 591 screening and surveillance colonoscopies were performed by 14 fellows. This included six, four and four fellows, in the first, second and third year of clinical training, respectively. Fellows were on rotation at the VAMC for a mean of 1.9 months (range 1 to 3 months) during the study period. The average ADR was 68.8% (95% CI 65.37 - 72.24). The average withdrawal time was 27.59 min (95% CI 23.45 - 31.73). The average cecal intubation rate was 99% (95% CI 98-100%). There was no significant difference between ADRs, cecal intubation rates, and withdrawal times at different levels of training; however, a trend toward swifter withdrawal times with advancing training was noted. CONCLUSIONS: ADR appears not to be a useful measure of competency for gastroenterology fellows. Consideration should be given to alternative metrics that could avoid bias and confounders.
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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 | 0.022 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".