Impact of osteopathic manipulative techniques on the management of dizziness caused by neuro-otologic disorders: Protocol for systematic review and meta-analysis
Bibliographic record
Abstract
Background Osteopathic manipulative treatment (OMT) has been increasingly adopted by osteopathic practitioners to treat dizziness from neuro-otologic disorders. However, no systematic review has investigated effectiveness of OMT on benefit outcomes and harm outcomes associated with OMT for these conditions. The current protocol will provide pooled effect estimates for effectiveness of OMT for neuro-otologic disorders. Methods The following databases will be searched: CINAHL, Embase, Ovid MEDLINE, AMED, Ovid Emcare, PEDro, PubMed, PsycINFO, OSTMED.DR, and CENTRAL. Eligible randomized controlled trials and observational studies published in English will be reviewed. Target outcomes are change in dizziness, frequency of falls, quality of life (QOL), disability, and return to work; harm outcomes are dropouts due to ineffectiveness, adverse effects, and all-cause dropout rates. Studies investigating individual types of OMT techniques will be pooled. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) assessment tool will be used for data synthesis and to assess evidence quality. We will report effect estimates with weighted mean differences or standardized mean difference (SMD) for continuous outcome and odds ratios with 95% confidence intervals for binary outcomes. Discussion Our findings will be of importance to patients and osteopathic practitioners and will identify key areas for future research.
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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 | 0.051 | 0.082 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.023 | 0.030 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.047 | 0.004 |
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".