Evaluation of a Particle Repositioning Maneuver Web‐Based Teaching Module
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To compare the pass rate of residents performing the Particle Repositioning Maneuver (PRM) after one of three interventions: 1) small group PRM instruction (SG); 2) standard classroom instruction (CI); and 3) Web-based learning module (WM). We hypothesize that our Web-based learning module is more effective than CI and as effective as SG. STUDY DESIGN: Prospective randomized control trial. METHODS: The study population includes all family medicine residents at the University of Western Ontario. On day 0, all subjects were tested. Residents were then randomized to one of three intervention groups: 1) SG, 2) CI, or 3) WM. On day 7, the residents were again tested. Observers were blinded to the intervention type. Testing (day 0 and day 7) was performed using the DizzyFIX (Clearwater Clinical Ltd., London, Ontario, Canada), a pass/fail test, and evaluation by a trained observer (correct or incorrect). RESULTS: There were no statistically significant differences in pass rates between the three groups before the interventions (DizzyFIX: P = .2096, observer: P = .3710). After the interventions, DizzyFIX testing pass rates were 50.0% SG, 60.0% CI and 100.0% WM (P = .3564). Observer testing pass rates were 85.7% SG, 28.6% CI, and 83.3% WM (P = .0431). CONCLUSIONS: This study demonstrated that our Web-based learning module for the PRM is comparable to small-group clinical instruction, and superior to standard classroom instruction for teaching the PRM when evaluated by a trained observer.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| 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".