CAR Peer Learning Guide
Bibliographic record
Abstract
Peer learning is a quality initiative used to identify potential areas of practice improvement, both on a patient level and on a systemic level. Opportunities for peer learning include review of prior imaging studies, evaluation of cases from multidisciplinary case conferences, and review of radiology trainees' call cases. Peer learning is non-punitive and focuses on promoting life-long learning. It seeks to identify and disseminate learning opportunities and areas for systems improvement compared to traditional peer review. Learning opportunities arise from peer learning through both individual communication of cases reviewed for routine work, as well as through anonymous presentation of aggregate cases in an educational format. In conjunction with other tools such as root cause analysis, peer learning can be used to guide future practice improvement opportunities. This guide provides definitions of terms and a synthetic evidence review regarding peer review and peer learning, as well as medicolegal and jurisdictional considerations. Important aspects of what makes an effective peer learning program and best practices for implementing such a program are presented. The guide is intended to be a living document that will be updated regularly as new data emerges and peer learning continues to evolve in radiology practices.
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 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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.309 | 0.284 |
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".