A Prospective Multi‐Center Implementation Study to Improve the Diagnosis and Treatment of Benign Paroxysmal Positional Vertigo
Bibliographic record
Abstract
BACKGROUND: Benign paroxysmal positional vertigo (BPPV) is the most common cause of vertigo, yet it remains underdiagnosed and undertreated in emergency departments (EDs). Despite evidence-based guidelines recommending bedside diagnostic maneuvers (Dix-Hallpike and supine roll test) and canalith repositioning maneuvers (CRMs), these are infrequently utilized, leading to unnecessary imaging, prolonged symptoms, and increased healthcare utilization. OBJECTIVE: This study aimed to implement an educational strategy to improve the diagnosis and treatment of BPPV in the ED by increasing adherence to guideline-based practices. METHODS: We conducted a multicenter interrupted time series study from August 2020 to September 2023. The intervention, developed using the CAN-Implement framework, included online training, quick-reference tools, and a mobile app. Due to the COVID-19 pandemic, in-person training was canceled. The primary clinical outcome was the proportion of patients receiving the appropriate CRM based on positional test results. Implementation outcomes included fidelity, appropriateness, adoption, penetration, and system impact, reported using the Standards for Reporting Implementation Studies (StaRI) guidelines. RESULTS: We included 1682 patients (1252 pre-intervention, 430 post-intervention). There was no significant change in the primary outcome (appropriate CRM use, OR = 1.08, 95% CI: 0.76-1.40). However, selective CT use improved (OR = 1.29, 95% CI: 1.09-1.49), supine roll testing increased from 14.2% to 23.5%, and neurology consults decreased from 7.1% to 4.0%. Documentation of diagnostic test descriptors improved, while neurological exam documentation declined. CONCLUSION: The intervention did not significantly increase appropriate CRM use but led to improvements in selective imaging, neurology consultation, and horizontal canal testing. Provision of educational tools alone was insufficient to overcome identified environmental barriers. To effectively improve BPPV management in the ED, future efforts should combine hands-on training with system-level supports and workflow integration.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".