Physical Activity Predicts Cardiorespiratory Fitness After Stroke: A Diagnostic Accuracy Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Stroke clinicians need access to cost-effective, accurate, and time-efficient tools that can assist with cardiorespiratory fitness (V̇O 2 peak) screening. The associations and diagnostic metrics between physical activity as measured by the Physical Activity Scale for Individuals with Physical Disabilities (PASIPD) and V̇O 2 peak among individuals ≥6 months post-stroke were evaluated. METHODS: This is a secondary analysis of an randomized controlled trial (RCT). Participants' baseline age, sex, gait speed, V̇O 2 peak, and PASIPD were included in adjusted logistic regression analyses. The association between the PASIPD (MET-hours/day) and V̇O 2 peak at 15, 12, and 18 mL/kg/min was evaluated to reflect the average, lower, and upper limits of V̇O 2 peak post-stroke, respectively. 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. A 1-unit increase in the PASIPD (MET-hours/day) was significantly associated with 21% increased odds of identifying an individual with a V̇O 2 peak ≥ 15 mL/kg/min (adjusted OR [aOR] = 1.21; 95% CI 1.07, 1.36; P = .002) with excellent area under the curve (AUC = 0.91; 95% CI 0.85, 0.97). Consistent associations were found for a V̇O 2 peak ≥ 12 mL/kg/min (aOR = 1.15; 95% CI 1.01, 1.14; P = .046) but not for V̇O 2 peak ≥ 18 mL/kg/min (aOR = 1.04; 95% CI 0.99, 1.10; P = .15). Unadjusted Youden PASIPD cut point of 8.9 MET-hours/day may identify individuals with a V̇O 2 peak ≥ 15 mL/kg/min post-stroke (AUC = 0.69; 95% CI 0.59, 0.79). DISCUSSION AND CONCLUSIONS: Clinicians may use the PASIPD to screen V̇O 2 peak impairments post-stroke.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".