Reliability and Validity of the Canadian Neurological Scale in Retrospective Assessment of Initial Stroke Severity
Bibliographic record
Abstract
BACKGROUND: Stroke severity is an important determinant of outcome, however, quantitative data on the initial neurological status might be lacking in retrospective studies. We wanted to assess the reliability and validity of the retrospective use of the Canadian Neurological Scale (CNS). METHODS: In 181 patients with validated stroke, two raters scored the CNS based on medical record review. We assessed interrater reliability and construct validity of the CNS. Predictive validity was assessed by the ability of the CNS to predict 30-day and 1-year mortality. RESULTS: Interrater reliability was high (kappa or weighted kappa 0.76-0.96). Correlations between similar items of prospective Scandinavian Stroke Scale scores and retrospective CNS scores ranged from 0.54 to 0.85. CNS total score was a strong predictor of death within 30 days and 1 year in multivariate models. CONCLUSIONS: The retrospective algorithm for the CNS had a high to substantial interrater reliability and predictive validity. Accordingly, in retrospective stroke studies using medical record information, the CNS can be a feasible instrument to adjust for differences in stroke severity.
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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 | 0.027 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 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".