National Institutes of Health Stroke Scale Item Profiles as Predictor of Patient Outcome
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: National Institutes of Health Stroke Scale (NIHSS) item profiles that were recently proposed may prove useful both clinically and for research studies. We aimed to validate the NIHSS item profiles in an acute cohort. METHODS: We conducted a retrospective analysis on pooled data from randomized clinical trials. We applied the latent class analysis probabilities of profile membership developed from the derivation study to obtain symptom grouping, a-NIHSS item profiles. We implemented an independent latent class analysis to derive secondary symptom grouping, b-NIHSS item profiles. Validation was performed by assessing the associations with outcomes and evaluating both sets of NIHSS item profiles' discrimination and calibration to the data. The outcomes evaluated included modified Rankin Scale (mRS; using the full distribution and dichotomized, mRS, 0-1) at day 90 and mortality by 90 days. RESULTS: We identified 10 271 patients. Ordinal analysis of mRS confirmed increased odds of better outcome across the profiles in a stepwise manner, adjusted for age and thrombolysis treatment, for each set of NIHSS item profiles. Similar patterns were observed for mRS 0 to 1, and inverse patterns were seen for mortality. The c-statistics of a-NIHSS and b-NIHSS item profiles for mRS 0 to 1 were similar at 0.71 (95% confidence interval, 0.70-0.72) and for mortality, 0.74 (0.73-0.75) and 0.75 (0.73-0.76), respectively. Calibration was good. CONCLUSIONS: These NIHSS item profiles identified using latent class analysis offer a reliable approach to capture the true response patterns that are associated with functional and outcome and mortality post stroke. This approach has the potential to enhance the clinical value of the overall NIHSS score.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".