Peyton's Four-Step Teaching Approach Improves Students’ Technique to Perform a Cranial Drawer Assessment but Does Not Improve Their Ability to Diagnose Cranial Cruciate Ligament Rupture in a Canine Stifle Joint Model
Bibliographic record
Abstract
Peyton's four-step teaching approach (PFSA) has been shown to be beneficial in teaching medical students practical skills for clinical practice. This study evaluated the effectiveness of PFSA in comparison to a standard method (ST) for teaching students to confidently perform the cranial drawer assessment and to identify cranial drawer in a canine stifle joint model that simulated different types of cranial cruciate ligament (CCL) integrity (complete rupture, partial rupture, and intact). Students were randomly allocated into two groups, taught how to perform a cranial drawer test with either PFSA or the ST, and tasked to assess CCL integrity in six models. The assessment was repeated 2 weeks later. Students taught with the PFSA had higher scores for their technique when compared with the ST group ( p < .01) for both intact and partial CCL rupture models at the initial assessment. The ability to correctly identify the CCL integrity in the models was not different between the ST and PFSA groups. Student confidence in being able to perform the cranial drawer test improved in both the ST and PFSA groups at the second assessment. While PFSA improved students’ technique and their confidence in assessing the CCL integrity, their ability to correctly identify CCL functional integrity in the stifle joint model was not improved. The benefits of using PFSA need to be balanced with the greater time it takes to perform.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".