An Evaluation of the Accuracy of Peer to Peer Surgical Teaching and the Role of the Peer Review Process
Bibliographic record
Abstract
Background: Peer to peer learning is a well-established learning modality which has been shown to improve learning outcomes, with positive implications for clinical practice. Surgical students from across Ireland were invited to upload learning points daily while paired with their peers in a peer-reviewing process. This study was designed to assess content accuracy and evaluate the benefit of the review process. Method: A reflective content sample was selected from the database representing all gastrointestinal (GI) surgical entries. All questions and answers were double corrected by four examiners, blinded to the “review” status of the entries. Statistical analysis was performed to compare accuracy between “reviewed” and “non-reviewed” entries. Results: There were 15,569 individual entries from 2009–2013, 2977 were GI surgery entries; 678 (23%) were peer reviewed. Marked out of 5, accuracy in the reviewed group was 4.24 and 4.14 in the non-reviewed group. This was not statistically different (p = 0.11). Accuracy did not differ between universities or grade of tutors. Conclusion: The system of student uploaded data is accurate and was not improved further through peer review. This represents an easy, valuable and safe method of capturing surgical oral ward based teaching.
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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 | 0.212 | 0.527 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".