Development and validation of an innovative tool for the assessment of biomechanical strategies: The Timed “Up and Go†– Assessment of Biomechanical Strategies (TUG-ABS) for individuals with stroke
Bibliographic record
Abstract
OBJECTIVE: To develop and validate a clinical tool based on the biomechanical strategies exhibited by people with hemiparesis due to stroke during the performance of the Timed "Up and Go" test. DESIGN/METHODS: The Timed "Up and Go" Assessment of Biomechanical Strategies (TUG-ABS) was developed for subjects with stroke, based on the analyses of 3 sources of information: published evidence; opinions of rehabilitation professionals; and observations of TUG performances, followed by a multi-step approach, which involved the investigation of the reliability, content, and criterion-related validity of the preliminary version. Content validity was established by an expert panel, whereas intra- and inter-rater reliability was established by two independent examiners. Criterion-related validity was established by comparing the TUG-ABS scores at the item level obtained by independent analyses of video observations and the gold standard motion analysis system. The final tool included the items, which showed acceptable values for these psychometric properties. RESULTS: The preliminary version consisted of 24 items with 3 response categories. Twenty-one items showed acceptable content validity (0.72 ≤ κ ≤ 1.00; p ≤ 0.01), 19 acceptable intra- and inter-rater reliability (0.36 ≤ κ ≤1.00; p ≤ 0.04), and 15 acceptable criterion-related validity (0.29 ≤ κ ≤ 1.00; p ≤ 0.04). CONCLUSION: The final developed 15-item TUG-ABS version proved to be valid and reliable for individuals with hemiparesis due to stroke, but it should be clinically validated before being used for clinical applications and research purposes.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".