Seeing better - Evidence based recommendations on optimizing colonoscopy adenoma detection rate
Bibliographic record
Abstract
Colorectal cancer is one of the three most frequent causes of cancer deaths in men and women in Europe and North America. Diagnosis and resection of adenomas has convincingly demonstrated its utility in diminishing colorectal cancer incidence. Therefore, colonoscopy is now the gold standard for colorectal cancer screening. But it is also known that colonoscopy effectiveness varies among endoscopists. Among different quality indicators, the most used is the adenoma detection rate (ADR) which is the percentage of average-risk patients for colorectal cancer who are found to have at least one adenoma or adenocarcinoma during a screening colonoscopy. There is compelling evidence supporting an inverse correlation between ADR and interval colorectal cancer (cancer found after a screening colonoscopy). Many factors such as quality of precolonoscopy preparation, additional observers, manoeuvres with the endoscope (second view, retroflexion, water inflation rather than air), time spent during withdrawal, changes in patient position, fold-flattener devices, new imaging or endoscopic modalities and use of intravenous or through the scope sprayed drugs, have been studied and developed with the aim of increasing the ADR. This reviews discusses these factors, and the current evidence, to "see better" in the colon and optimize ADR.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".