Assessment of Walking Speed and Distance Post-Stroke Increases After Providing a Theory-Based Toolkit
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: While underutilized, poststroke administration of the 10-m walk test (10mWT) and 6-minute walk test (6MWT) can improve care and is considered best practice. We aimed to evaluate provision of a toolkit designed to increase use of these tests by physical therapists (PTs). METHODS: In a before-and-after study, 54 PTs and professional leaders in 9 hospitals were provided a toolkit and access to a clinical expert over a 5-month period. The toolkit comprised a guide, smartphone app, and video, and described how to set up walkways, implement learning sessions, administer walk tests, and interpret and apply test results clinically. The proportion of hospital visits for which each walk test score was documented at least once (based on abstracted health records of ambulatory patients) were compared over 8-month periods pre- and post-intervention using generalized mixed models. RESULTS: Data from 347 and 375 pre- and postintervention hospital visits, respectively, were analyzed. Compared with preintervention, the odds of implementing the 10mWT were 12 times greater (odds ratio [OR] = 12.4, 95% confidence interval [CI] 5.8, 26.3), and of implementing the 6MWT were approximately 4 times greater (OR = 3.9, 95% CI 2.3, 6.7), post-intervention, after adjusting for hospital setting, ambulation ability, presence of aphasia and cognitive impairment, and provider-level clustering. Unadjusted change in the percentage of visits for which the 10mWT/6MWT was documented at least once was smallest in acute care settings (2.0/3.8%), and largest in inpatient and outpatient rehabilitation settings (28.0/19.9% and 29.4/23.4%, respectively). DISCUSSION AND CONCLUSIONS: Providing a comprehensive toolkit to hospitals with professional leaders likely contributed to increasing 10mWT and 6MWT administration during inpatient and outpatient stroke rehabilitation.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A390 ).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".