Treatment and recurrence of traumatic versus idiopathic benign paroxysmal positional vertigo: a meta-analysis
Bibliographic record
Abstract
Background: So far, there has been a controversy surrounding repositioning difficulty and recurrence rate between traumatic benign paroxysmal positional vertigo (t-BPPV) and idiopathic BPPV (i-BPPV).Objectives: This meta-analysis was aimed to explore whether or not the differences between t-BPPV and i-BPPV in the repositioning difficulty and recurrence rate existed.Material and methods: A literature search was performed in the databases including Pubmed, Embase, CENTRAL, which completed in 21 January 2019, with no restriction of publication language. Relative risk (RR) of number of repositioning maneuvers and the recurrence rate was calculated with its 95% confidence interval. Sensitive analysis was performed simultaneously.Results: Six retrospective cohort studies were included in our meta-analysis, including 865 t-BPPV patients and 3027 i-BPPV patients. All studies were high quality according to Newcastle-Ottawa Scale (NOS) assessment. Patients with t-BPPV required more repositioning maneuvers for resolution than those with i-BPPV (RR = 3.27, 95% CI = 1.88–5.69, p < .0001), and the recurrence rate of t-BPPV was higher than that of i-BPPV (RR = 2.91, 95% CI = 2.04–4.14, p < .00001).Conclusions and significance: Compared with i-BPPV, patients with t-BPPV require more repositioning maneuvers to resolve, and the recurrence of t-BPPV was more frequent.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.052 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".