Estimating the Accuracy of the Chedoke–McMaster Stroke Assessment Predictive Equations for Stroke Rehabilitation
Bibliographic record
Abstract
PURPOSE: To estimate the predictive accuracy and clinical usefulness of the Chedoke-McMaster Stroke Assessment (CMSA) predictive equations. METHOD: A longitudinal prognostic study using historical data obtained from 104 patients admitted post cerebrovascular accident was undertaken. Data were abstracted for all patients undergoing rehabilitation post stroke who also had documented admission and discharge CMSA scores. Published predictive equations were used to determine predicted outcomes. To determine the accuracy and clinical usefulness of the predictive model, shrinkage coefficients and predictions with 95% confidence bands were calculated. RESULTS: Complete data were available for 74 patients with a mean age of 65.3±12.4 years. The shrinkage values for the six Impairment Inventory (II) dimensions varied from -0.05 to 0.09; the shrinkage value for the Activity Inventory (AI) was 0.21. The error associated with predictive values was greater than ±1.5 stages for the II dimensions and greater than ±24 points for the AI. CONCLUSIONS: This study shows that the large error associated with the predictions (as defined by the confidence band) for the CMSA II and AI limits their clinical usefulness as a predictive measure. Further research to establish predictive models using alternative statistical procedures is warranted.
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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) | 0.000 | 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".