P17 Clinical use of the star excursion balance test to assess dynamic postural stability deficits following a lateral ankle sprain in prognosis of chronic ankle instability
Bibliographic record
Abstract
Study Design Systematic Review. Objective Our objective was to conduct a systematic review of the literature to determine if the Star Excursion Balance Test (SEBT) demonstrates equivalent dynamic postural stability deficits post-LAS to those identified in instrumented tests not usually available to clinicians. Background Identifying postural stability deficits may differentiate those with and without chronic ankle instability (CAI) post-lateral ankle sprain (LAS). Advanced technology-based tests using force plates or kinematic data for postural stability and sensorimotor deficits are validated in patients post-LAS. There is a need for an accessible, non-instrumented test to identify dynamic postural stability deficits and improve assessment, prognosis and prevention of factors leading to CAI. Methods 11 databases were systematically searched from the earliest record until December 2016 to identify studies of postural stability testing in participants post LAS, utilising instrumented kinetic and/or kinematic data as well as SEBT performance. The QUIPS tool was used to quality rate the reviewed. Results From the initial 1148 studies, five not previously systematically reviewed met our criteria. Four studies of low overall risk of bias and one of moderate risk conclude that SEBT is valid to identify postural deficits post-LAS. Conclusion Based on the literature reviewed, SEBT seems to be a practical and valid tool for clinicians to assess postural stability deficits in patients post-LAS.
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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 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".