Higher adenoma detection, sessile serrated lesion detection and proximal sessile serrated lesion detection are associated with physician specialty and performance on Direct Observation of Procedural Skills
Bibliographic record
Abstract
OBJECTIVE: Adenoma detection rate (ADR) and sessile serrated lesion detection rate (SSLDR) vary among physicians. We sought to determine physician characteristics associated with ADR and SSLDR in a population-based colon screening programme. DESIGN: Retrospective study of 50-74 year olds with positive faecal immunochemical test and colonoscopy from 15/11/2013 to 31/12/2018. Physician characteristics included: gender, specialty, year and country of medical school graduation, colonoscopy volume and Direct Observation of Procedural Skills (DOPS) performance. Multivariable regression was performed on the following dependent variables: ADR, advanced ADR, proximal and distal ADR, SSLDR, proximal and distal SSLDR. RESULTS: 2000 10.48, 95% CI 1.30 to 1.69 compared with <1980) and DOPS performance (OR for lowest DOPS performance 0.64, 95% CI 0.50 to 0.82 compared with highest DOPS performance). SSLDR was associated with gastroenterology (OR for general surgery 0.89, 95%, CI 0.81 to 0.97; OR for general/family/internal medicine 0.67, 95% CI 0.49 to 0.92) and DOPS performance (OR for lowest DOPS performance 0.71, 95% CI 0.51 to 0.99 compared with highest DOPS performance). Proximal SSLDR was associated with gastroenterology (OR for general surgery 0.90, 95% CI 0.82 to 0.99; OR for general/family/internal medicine 0.69, 95% CI 0.50 to 0.97) and DOPS performance (OR for lowest DOPS performance 0.68, 95% CI 0.47 to 0.99). CONCLUSION: Higher ADR, SSLDR and proximal SSLDR was associated with gastroenterology specialty and improved performance on DOPS.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".