The cognitive burden of stroke emerges even with an intact NIH Stroke Scale Score: a cohort study
Bibliographic record
Abstract
BACKGROUND: We aim to facilitate recognition of the cognitive burden of stroke by describing the parallels between cognitive deficits and the National Institutes of Health Stroke Scale (NIHSS), a widely used measure of stroke severity. METHODS: A consecutive cohort of 223 working-age patients with an acute first-ever ischaemic stroke was assessed neuropsychologically within the first weeks after stroke and at a 6-months follow-up visit and compared with 50 healthy demographic controls. The NIHSS was administered at the time of hospital admittance and upon discharge from the acute care unit. The associations between total NIHSS scores and domain-specific cognitive deficits were analysed correlatively and with a binary logistic regression. RESULTS: Of the NIHSS measurements (admittance median=3, range 0-24; discharge median=1, range 0-13), the total score at the time of discharge had systematically stronger correlations with cognitive impairment. Adjusted for demographics, the NIHSS discharge score stably predicted every cognitive deficit with ORs ranging from 1.4 (95% CI 1.2 to 1.6) for episodic memory to 1.9 (95% CI 1.5 to 2.3) for motor skills. The specificities of the models ranged from 89.5-97.7%, but the sensitivities were as low as 11.6-47.9%. Cognitive deficits were found in 41% of patients with intact NIHSS scores and in all patients with NIHSS scores ≥4, a finding that could not be accounted for by confounding factors. CONCLUSIONS: Cognitive deficits were common even in patients with the lowest NIHSS scores. Thus, low NIHSS scores are not effective indicators of good cognitive outcomes after stroke.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".