Best Quantitative Tools for Assessing Static and Dynamic Standing Balance after Stroke: A Systematic Review
Bibliographic record
Abstract
Purpose: Our objective was to examine the psychometric qualities (reliability and validity) and clinical utility of quantitative tools in measuring the static and dynamic standing balance of individuals after stroke. Method: We searched four databases (PubMed/MEDLINE, PEDro, Embase, and CINAHL) for studies published from January 2018 through September 2019 and included those that assessed the psychometric properties of standing balance tests with an adult stroke population. We evaluated the quality of the studies using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist and assessed each test on a utility assessment scale. Results: A total of 22 studies met the inclusion criteria, and 18 quantitative tools for assessing static or dynamic standing balance of individuals with stroke were analyzed. Findings support good or excellent reliability for all tests, whereas correlations for validity ranged from weak to strong. Study quality was variable. Dynamic balance tests had better clinical utility scores than static ones. Five tests had complete psychometric analyses: quiet standing on a force platform, five-step test, sideways step, step length, and turn tests.
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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 | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 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".