Resident Practice Audit in Gastroenterology (RPAGE): an innovative approach to trainee evaluation and professional development in medicine
Bibliographic record
Abstract
BACKGROUND: The Resident Practice Audit in Gastroenterology (RPAGE) captures assessments of knowledge, professionalism, and technical skills, in real time. This brief report describes this innovative instrument and aspects of its utility. METHODS: Assessment data on colonoscopy, endoscopy, and sigmoidoscopy procedures in 2016 were submitted to a repeated measures ANOVA with six within subjects' assessments and one between subjects' factor of year of specialization to evaluate construct validity. The validity hypothesis tested was that more experienced residents would be rated higher than less experienced residents. Reliability was assessed using Cronbach's alpha. RESULTS: The proportion of completed assessments was relatively low (9 to 22%). Overall reliability was high (α >0.8). There was evidence of validity as global ratings indicated higher competence for senior residents at colonoscopy (1.6) and upper endoscopy (1.4) than for more junior residents (1.9 and 2.1 respectively). These differences were significant for both colonoscopy, (F (1, 282) = 14.8, p <0.001) and endoscopy, F (1, 136) = 56.9, p <0.001. CONCLUSION: These findings suggest RPAGE is an acceptable electronic log of practice data, but may not be acceptable for workplace based assessment. A key next step will be to evaluate how information collected through RPAGE can help inform resident competency committees.
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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 | 0.045 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".