Reliability and validity of the Canadian neurological scale, Thai version.
Bibliographic record
Abstract
BACKGROUND: The Canadian Neurological Scale (CNS) is one of the most reliable stroke severity assessment scales. There is a strong need for a simple and well validated stroke severity assessment scale among Thais. OBJECTIVE: To translate and perform a reliability and validity study of the Canadian Neurological Scale, Thai version (CNS-T). MATERIAL AND METHOD: Forward and backward translations of the original CNS version were independently performed. The final version of the CNS-T was prospectively tested for reliability and validity in acute ischemic stroke setting. Consecutive series of acute stroke patients were assessed by one of the six raters from three different types of healthcare providers: 2 stroke nurses, 2 internal medicine residents and 2 stroke fellows. Each patient was independently assessed twice at 3 weeks interval using video tape by all raters. Extent of infarction was measured by MRI lesion volume. Clinical outcome at 3 months was measured using modified Rankin Score (mRS). Correlation among the CNS-T and 3-mo mRS and MRI lesion volume were assessed. Inter and intra-observer reliabilities were evaluated. RESULTS: A total of 38 patients were enrolled. Median CNS-T was 8.5. Intra-observer reliability demonstrated a high agreement with an intraclass correlation (ICC) of 0.99, 0.97, 0.98, 0.96, 0.93 and 0.98 for 2 stroke fellows, 2 internal medicine residents and 2 stroke nurses respectively. Inter-observer reliability between the 6 raters was excellent: ICC 0.87 (95% CI; 0.81-0.92). The Spearman rank correlation coefficient was -0.55 (p = 0.001) between the initial CNS-T score versus initial MRI lesion volume and -0.61 (p < 0.001) between the initial CNS-T score versus 3-mo mRS. CONCLUSION: The CNS-T can be performed by trained nurses, internists and neurologists with an excellent reliability. The CNS-T is a valid and simple clinical tool for stroke severity assessment among Thais.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".