National Institutes of Health Stroke Scale Score is an Unreliable Predictor of Perfusion Deficits in Acute Stroke
Bibliographic record
Abstract
BACKGROUND: Perfusion-weighted magnetic resonance imaging is not routinely used to investigate stroke/transient ischemic attack. Many clinicians use perfusion-weighted magnetic resonance imaging selectively in patients with more severe neurological deficits, but optimal selection criteria have never been identified. AIMS AND/OR HYPOTHESIS: We tested the hypothesis that a National Institutes of Health Stroke Scale score threshold can be used to predict the presence of perfusion-weighted magnetic resonance imaging deficits in patients with acute ischemic stroke/transient ischemic attack. METHODS: National Institutes of Health Stroke Scale scores were prospectively assessed in 131 acute stroke/transient ischemic attack patients followed by magnetic resonance imaging, including perfusion-weighted magnetic resonance imaging within 72 h of symptom onset. Patients were dichotomized based on the presence or absence of perfusion deficits using a threshold of Tmax (time to peak maps after the impulse response) delay ≥four-seconds and a hypoperfused tissue volume of ≥1 ml. RESULTS: Patients with perfusion deficits (77/131, 59%) had higher median (interquartile range) National Institutes of Health Stroke Scale scores (8 [12]) than those without perfusion deficits (3 [4], P < 0.001). A receiver operator characteristic analysis indicated poor to moderate sensitivity of National Institutes of Health Stroke Scale scores for predicting perfusion deficits (area under the curve = 0.787). A National Institutes of Health Stroke Scale score of ≥6 was associated with specificity of 85%, but sensitivity of only 69%. No National Institutes of Health Stroke Scale score threshold identified all cases of perfusion-weighted magnetic resonance imaging deficits with sensitivity >94%. CONCLUSIONS: Although higher National Institutes of Health Stroke Scale scores are predictive of perfusion deficits, many patients with no clinically detectable signs have persisting cerebral blood flow changes. A National Institutes of Health Stroke Scale score threshold should therefore not be used to select patients for perfusion-weighted magnetic resonance imaging. Perfusion-weighted magnetic resonance imaging should be considered in all patients presenting with acute focal neurological deficits, even if these deficits are transient.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".