Standardized measurement of balance and mobility post-stroke: Consensus-based core recommendations from the third Stroke Recovery and Rehabilitation Roundtable
Bibliographic record
Abstract
BACKGROUND: Mobility is a key priority for stroke survivors. Worldwide consensus of standardized outcome instruments for measuring mobility recovery after stroke is an essential milestone to optimize the quality of stroke rehabilitation and recovery studies and to enable data synthesis across trials. METHODS: defined survey-based approach followed by group discussions. The group agreed on balance- and mobility-related definitions and recommended a core set of outcome measure instruments for lower extremity motor function, balance and mobility, biomechanical metrics, and technologies for measuring quality of movement. RESULTS: Selected measures included the Fugl-Meyer Motor Assessment lower extremity subscale for motor function, the Trunk Impairment Scale for sitting balance, and the Mini Balance Evaluation System Test (Mini-BESTest) and Berg Balance Scale (BBS) for standing balance. The group recommended the Functional Ambulation Category (FAC, 0-5) for walking independence, the 10-meter Walk Test (10 mWT) for walking speed, the 6-Minute Walk Test (6 MWT) for walking endurance, and the Dynamic Gait Index (DGI) for complex walking. An FAC score of less than three should be used to determine the need for an additional standing test (FAC < 3, add BBS to Mini-BESTest) or the feasibility to assess walking (FAC < 3, 10 mWT, 6 MWT, and DGI are "not testable"). In addition, recommendations are given for prioritized kinetic and kinematic metrics to be investigated that measure recovery of movement quality of standing balance and walking, as well as for assessment protocols and preferred equipment to be used. CONCLUSIONS: The present recommendations of measures, metrics, technology, and protocols build on previous consensus meetings of the International Stroke Recovery and Rehabilitation Alliance to guide the research community to improve the validity and comparability between stroke recovery and rehabilitation studies as a prerequisite for building high-quality, standardized "big data" sets. Ultimately, these recommendations could lead to high-quality, participant-specific data sets to aid the progress toward precision medicine in stroke rehabilitation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.291 | 0.245 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.018 | 0.013 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".