Systematic Review of Bilateral Benign Paroxysmal Positional Vertigo
Bibliographic record
Abstract
OBJECTIVES: To evaluate the success rates of canalith repositioning maneuvers (CRM) in the treatment of patients diagnosed with bilateral benign paroxysmal positional vertigo (BiBPPV). STUDY DESIGN: Systematic review. METHODS: A comprehensive search of only English studies in PubMed, Ovid (1946 to the present), and Embase (1974 to the present) databases was done up until January 1, 2021. Studies that diagnosed patients with BiBPPV specifically and evaluated the CRM success from all published years were included. Studies were excluded if follow-up was less than 6 months or if they failed to distinguish BiBPPV from ipsilateral multi-canal BPPV. A total of nine studies were included with a total study population of 325 patients. Included studies were evaluated for bias with the National Institutes of Health (NIH) Study Quality Assessment Tool. Success rates of CRM, number of treatments required, and disease recurrence rates were extracted. RESULTS: The overall success rate was compiled using a fixed-effect binary inverse variance model and was 95.2% (CI: 92.9%-97.5%). A qualitative review suggested treating the more affected side first on separate visits until resolved, followed by contralateral treatment (recommendation). The mean number of treatments was 2.9 (CI: 2.4-3.4), and the recurrence rate was 19.8% (CI: 11.7%-27.9%). There was a higher proportion of trauma etiology of BiBPPV compared to unilateral, with an odds ratio of 8.9 (CI: 5.1-15.3). CONCLUSIONS: Overall, this meta-analysis shows high success rates for CRM in the treatment of BiBPPV. Rates are similar to CRM efficacy for unilateral BPPV. Laryngoscope, 132:640-647, 2022.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".