Walking while talking: validation in older adults with lower-limb amputation
Bibliographic record
Abstract
Background: Walking while talking (WWT) is a dual-task (cognitive and motor) performance test that has not yet been validated in older adults with lower-limb amputation (LLA). WWT is composed of two sections: WWT-simple (walking while reciting every letter of the alphabet) and WWT-complex (walking while reciting every other letter of the alphabet). Objective: To determine the validity, and provide normative data and a new scoring system for the WWT test in older adults with LLA. Study design: Baseline cross-sectional data were collected from 56 community-living older adults (≥50 years old) with LLA at rehabilitation hospitals in Vancouver, BC, London, ON, and Edmonton, AB, Canada. Methods: Time and number of recited letters and errors during both sections were recorded. A new score was developed based on all these variables. Correlations of the recorded times with the Activities-specific Balance Confidence (ABC) scale and the 2-Minute Walk Test (2MWT) score were used to evaluate the validity of the WWT test. Results: As hypothesized, the times for both sections of the WWT were negatively correlated with both the ABC and 2MWT scores. Positive correlations were observed between the scores developed for both WWT sections with the 2MWT score. Conclusion: Times taken to complete each section of the WWT test were correlated with the 2MWT and the ABC scale scores, providing evidence for the validity of the WWT test. We also provided a scoring method that is more objective and in line with the dual-task nature of the test.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".