Prognostic Value of Stroke Severity Measured by National Institutes of Health Stroke Scale Score for Post-Stroke Neuropsychiatric Outcomes in a Hispanic Population
Bibliographic record
Abstract
Background: Stroke is a leading cause of disability and mortality. Neuropsychiatric complications are frequent, and stroke severity measured by National Institutes of Health Stroke Scale (NIHSS) may help identify patients at risk for adverse neuropsychiatric outcomes. The objective was to analyze whether stroke severity measured by the NIHSS score is associated with the development of neuropsychiatric complications after stroke in a Hispanic population. Methods: This observational, analytical, retrospective study included 170 adults with CT-confirmed stroke treated in 2024 at a tertiary hospital in Guayaquil. Stroke severity (NIHSS), clinical variables, and neuropsychiatric outcomes were analyzed using chi-square tests, Mann-Whitney U, and multivariable logistic regression, adhering to STROBE and ethical standards (SPSS version 27.0). Results were expressed as adjusted odds ratios (ORs) with 95% confidence intervals (CIs), considering P < 0.05 and including only models with an events-per-variable (EPV) ratio ≥ 6. Results: A total of 170 adults (mean age 61.32 ± 14.62 years) were studied; 63.5% were male. Ischemic strokes comprised 56.5% and hemorrhagic 43.5%. Median NIHSS at admission was 7 (interquartile range (IQR) 4 - 13); categories: no deficit 6.5%, mild 25.9%, moderate 48.8%, moderate-severe 8.2%, severe 10.6%. Overall, 42.4% (n = 72) developed neuropsychiatric manifestation: cognitive impairment and sleep disturbances each occurred in 22.4%, behavioral disorders in 20.0%, depression in 15.30%, anxiety in 8.20%, and psychosis in 4.70%. Chi-square testing showed significant associations for moderate strokes with sleep disturbances (63.2%, P = 0.024), cognitive impairment (68.4%, P = 0.029), and behavioral disorders (76.5%, P = 0.003). Mann-Whitney comparisons indicated higher median NIHSS in patients with depression (12 vs. 7, P = 0.014), sleep disturbances (8 vs. 6, P = 0.025), and behavioral disorders (12 vs. 6, P = 0.002). Multivariable logistic regression revealed that younger age (Exp(B) = 0.957, P = 0.046), female sex (Exp(B) = 9.801, P < 0.001), and higher NIHSS scores (Exp(B) = 1.101, P = 0.010) independently predicted depression. Higher NIHSS scores also predicted sleep disturbances (Exp(B) = 1.072, P = 0.047) and behavioral disorders (Exp(B) = 1.071, P = 0.036), with female sex acting as a protective factor for behavioral disorders (Exp(B) = 0.227, P = 0.012). Cognitive impairment was associated with shorter time to onset (Exp(B) = 0.997, P = 0.037). Conclusion: The NIHSS scale proved to be a useful tool not only for measuring the initial severity of stroke, but also for identifying the risk of developing neuropsychiatric complications.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".