The Canadian Neurological Scale and the NIHSS: Development and Validation of a Simple Conversion Model
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Bibliographic record
Abstract
BACKGROUND: The Canadian Neurological Scale (CNS) and the National Institutes of Health Stroke Scale (NIHSS) are among the most reliable stroke severity assessment scales. The CNS requires less extensive neurological evaluation and is quicker and simpler to administer. OBJECTIVE: Our aim was to develop and validate a simple conversion model from the CNS to the NIHSS. METHODS: A conversion model was developed using data from a consecutive series of acute-stroke patients who were scored using both scales. The model was then validated in an external dataset in which all patients were prospectively assessed for stroke severity using both scales by different observers which consisted of neurology residents or stroke fellows. RESULTS: In all, 168 patients were included in the model development, with a median age of 73 years (20-94). Men constituted 51.8%. The median NIHSS score was 6 (0-31). The median CNS score was 8.5 (1.5-11.5). The relationship between CNS and NIHSS could be expressed as the formula: NIHSS = 23 - 2 x CNS. A cohort of 350 acute-stroke patients with similar characteristics was used for model validation. There was a highly significant positive correlation between the observed and predicted NIHSS score (r = 0.87, p < 0.001). The predicted NIHSS score was on average 0.61 higher than the observed NIHSS score (95% CI = 0.31-0.91). CONCLUSIONS: The CNS can be reliably converted to the NIHSS using a simple conversion formula: NIHSS = 23 - 2 x CNS. This finding may have a practical impact by permitting reliable comparisons with NIHSS-based evaluations and simplifying the routine assessment of acute-stroke patients in more diverse settings.
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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 it