An iPhone-Assisted Particle Repositioning Maneuver for Benign Paroxysmal Positional Vertigo (BPPV): A Prospective Randomized Study
Bibliographic record
Abstract
PURPOSE: The Epley particle repositioning maneuver (PRM) is an effective treatment for benign paroxysmal positional vertigo (BPPV), the most common cause of peripheral vertigo in primary care settings. The goal of this study was to determine whether the use of an iPhone application (DizzyFIX; Clearwater Clinical Ltd, Ottawa, Ontario, Canada) by medical students had a significant impact on the performance of the PRM. METHODS: We recruited senior medical students who had previously been trained in the management of BPPV and asked them to perform the PRM on a healthy volunteer. One half of the students used a real iPhone application, whereas the others used a sham application. The PRM performance scores of the 2 groups were compared. RESULTS: iPhone application users scored significantly higher on their PRM performance compared with controls (P < .0001) and performed the PRM significantly more slowly (P < .0001). CONCLUSIONS: Senior medical students performed a more correct PRM when assisted by the iPhone application. This application represents a significant improvement from standard medical school training using written instructions. Family physicians could also use this iPhone application for the quick and effective treatment of BPPV.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".