Physical activity predicts fitness and walking capacity after stroke: a diagnostic accuracy study
Bibliographic record
Abstract
Abstract Background and Purpose Clinicians need access to accurate self-reported tools that can assist with screening individuals who are at risk for fitness (V̇O 2 peak) and walking impairments (e.g., 6-minute walk test [6MWT] distance) post-stroke. The associations and diagnostic metrics between self-reported physical activity as measured by the Physical Activity Scale for People with Disabilities (PASIPD, MET-hours/week) and V̇O 2 peak (≥ 15 mL/kg/min) and 6MWT (≥ 350 m) among ≥ 6 months post-stroke were evaluated. Methods This is a secondary analysis from an aerobic exercise RCT. Participants’ age, sex, V̇O 2 peak, 6MWT distance and the PASIPD were collected at baseline. Multivariable logistic regression analyses evaluated the association between V̇O 2 peak (≥ 15 mL/kg/min), 6MWT (≥ 350 m) and the PASIPD (MET-hours/week). Predicted classifications and the Youden index identified cut points of the PASIPD. Results Eighty-five participants (n=53 males, aged 65.1 ± 9.5 years, 1.8 ± 1.2 years post-stroke) were included. Significant associations were found whereby one-unit increase in the PASIPD (MET-hours/week) was associated with a 21% increase in the odds of having a V̇O 2 peak ≥ 15 mL/kg/min [aOR = 1.21; 95% CI 1.07, 1.36, p=0.002] and 14% increase in the odds of having a 6MWT ≥ 350 m [aOR = 1.14; 95% CI 1.05, 1.23, p=0.001] with excellent area under the curve values (AUC: 0.82-0.93). Youden-derived PASIPD cut points of 8.9 and 10.6 MET-hours/week may identify individuals with fitness (≥ 15 mL/kg/min) and walking impairments (≥ 350 m)post-stroke (AUC: 0.69-0.71). Discussion and Conclusions Clinicians may use the self-reported PASIPD to identify individuals with fitness and walking impairments post-stroke.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".