Walking speed over 10 metres overestimates locomotor capacity after stroke
Bibliographic record
Abstract
OBJECTIVE: To examine 10-m comfortable walking speed and 6-minute distance in healthy individuals and individuals after stroke and to assess the level of disability associated with poor walking endurance after stroke. DESIGN: Descriptive study in which comfortable walking speed over 10 m and distance covered in 6 minutes (6-minute walk test) were compared between healthy subjects and subjects after stroke. SUBJECTS: Twelve healthy subjects and 14 subjects after stroke. MAIN OUTCOME MEASURES: Walking speed and 6-minute distances were compared between groups. In addition, for each group, actual distance walked in 6 minutes was compared with the distance predicted by the 10-m walking speed test and the distance predicted by normative reference equations. RESULTS: Subjects after stroke had significant reductions in 10-m speed and 6-minute distance compared with healthy subjects (p < 0.05). Subjects after stroke were not able to maintain their comfortable walking speed for 6 minutes, whereas healthy subjects walked in excess of their comfortable speed for 6 minutes. The average distance walked in 6 minutes by individuals after stroke was only 49.8+/-23.9% of the distance predicted for healthy individuals with similar physical characteristics. CONCLUSION: In our subjects after stroke, walking speed over a short distance overestimated the distance walked in 6 minutes. Both walking speed and endurance need to be measured and trained during rehabilitation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".