Development of a Telephone Interview Version of the Chedoke-McMaster Stroke Assessment Activity Inventory
Bibliographic record
Abstract
Purpose: To develop a telephone version of the Chedoke-McMaster Stroke Assessment Activity Inventory (CMSA–AI) and estimate the test–retest reliability, interrater reliability (between participant and proxy), and construct validity of the scores for individuals with stroke. Methods: Adults with stroke and their caregivers or proxies were included. Participants were assessed with the CMSA–AI at discharge from a stroke rehabilitation unit and interviewed using the telephone version (TCMSA–AI). Two months after discharge, participants were evaluated with the CMSA–AI and interviewed over the phone using the TCMSA–AI on two occasions 2–3 days apart. Proxies were interviewed with the TCMSA–AI within another 2–3 days. Results: The mean age of the 53 participants with stroke was 62 years; 59% were male; 43% had right-side hemiparesis; 42 completed follow-up interviews; and 18 had proxies who also participated. Test–retest reliability showed an intra-class correlation coefficient of 0.98 (95% CI: 0.96, 0.99) for the total score, 0.96 (95% CI: 0.91, 0.98) for the Gross Motor Function Index, and 0.96 (95% CI: 0.91, 0.98) for the Walking Index, and an interrater reliability (between participant and proxy) of 0.75 (95% CI: 0.28, 0.90) for total score. Spearman's rho correlation between CMSA–AI and TCMSA–AI total scores was 0.62 (lower-sided 95% CI: 0.42) at discharge and 0.90 (lower-sided 95% CI: 0.82) at 2 months after discharge. Correlations between the change scores of the CMSA–AI and TCMSA–AI were 0.50 or lower. Conclusion: There is potential for remote evaluation of the functional mobility of individuals with stroke in research and clinical settings.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".