Defining and Applying Locally Relevant Benchmarks for the Adenoma Detection Rate
Bibliographic record
Abstract
INTRODUCTION: The adenoma detection rate (ADR) is the best validated colonoscopy performance quality indicator. The ASGE/ACG Task Force on Colonoscopy Quality set an ADR benchmark of ≥25% in a mixed male/female population. We propose a novel means for defining locally relevant ADR benchmarks using data from the population of interest and for applying ADR benchmarks using 95% confidence intervals (CIs) of an endoscopist's ADR. We further propose that ADR benchmarks should be raised to reflect what can be achieved by high-performing endoscopists. METHODS: We used endoscopists' performance in a baseline year to develop and apply benchmarks in an assessment year. We defined assessment year benchmarks (Minimally Acceptable, Standard of Care, and Aspirational) based on the average ADR of performance groups defined by baseline year ADR quartiles. We demonstrated the use of these benchmarks in endoscopists performing screening colonoscopies by determining if the upper bound of the 95% CI of the endoscopist's ADR included the ADR benchmark. RESULTS: The study included 8,492 colonoscopies (mean ADR 29%) in 2014 and 5,193 colonoscopies (mean ADR 32%) in 2015, completed at a regional screening center in Calgary, Canada. The Minimally Acceptable, Standard of Care, and Aspirational benchmarks for 2015 were 25%, 30%, and 39%, respectively. The 95% CI of the ADR of 1 (3%), 3 (10%), and 12 (39%) endoscopists did not include the benchmark. DISCUSSION: We have proposed methods for defining and applying benchmarks for ADR in average-risk patients that go beyond the "minimally acceptable" threshold currently recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".