Supplementary Material for: The Effect of Auditory Cues on Static Postural Control: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: The purpose of this systematic review and meta-analysis was to summarize the results related to the effects of auditory cues on static postural control. Methods: MEDLINE/PubMed, EMBASE, SCOPUS, LILACS, CINAHL, CENTRAL, Web of Science, PEDro, and Google Scholar were searched from inception until September 2020. Risk of bias was evaluated by both reviewers using Newcastle-Ottawa Quality Assessment Scale (NOS). Results: Twelve studies with 403 participants were included in the review and 9 studies with 305 participants in the meta-analysis. Results show that auditory cues have significant effects on postural sway in the anterior-posterior direction (p = 0.001), postural sway in the medial-lateral direction (p = 0.001), and static balance (p = 0.001). A low to high heterogeneity was observed across all comparisons. Conclusions: Results of this meta-analysis revealed that auditory cues decrease postural sway in the anterior-posterior and medial-lateral direction; it also improves static balance. Thus, it can be concluded that auditory cues improve static postural control. Our results suggest that the auditory system can be a determinant of static postural control along with other sensory systems including visual, vestibular, and proprioception systems. Also, this study implies that auditory cues can be a significant therapeutic approach to improve static postural control.
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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 | 0.008 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.636 | 0.038 |
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".