Assessing Gait Function in Lower Limb Rehabilitation: The Role of the Gait Analysis and Motion Score (GAMS)
Bibliographic record
Abstract
Background: Assessment of gait function is crucial for optimising rehabilitation outcomes. The gait analysis and motion score (GAMS) summarises qualitative and quantitative gait parameters from treadmill-based analyses to evaluate functional walking status. Objectives: To assess the sensitivity of the GAMS for detecting short-term changes, its test–retest reliability, and its correlation with the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and the Timed Up and Go (TUG) test. Methods: A retrospective analysis of 94 inpatient rehabilitation patients with hip, knee, or ankle impairments was performed. Changes in GAMS, WOMAC, and TUG scores and their interrelationships were assessed at both admission and discharge. Results: GAMS, WOMAC, and TUG showed significant improvements over time, with medium effect sizes (η2 = 0.303 to 0.434; p < 0.001). No significant differences in outcome measures were observed between groups. Moderate to strong correlations were found between pre- and post-rehabilitation scores for GAMS, TUG, and WOMAC (r = 0.58 to r = 0.90), indicating good test–retest reliability. A significant low negative correlation between GAMS and TUG was observed for all patients at admission (r = −0.30, p = 0.003) and discharge (r = −0.26, p = 0.030). No significant correlations were observed between GAMS and WOMAC in any patient group. Baseline GAMS scores significantly influenced change scores. Conclusions: GAMS is a sensitive and reliable tool for detecting short-term changes in gait parameters. GAMS and TUG assess related but distinct constructs, with GAMS and WOMAC assessing different domains of gait function. Therefore, GAMS provides complementary information not captured by WOMAC or TUG.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".