The Reliability and Sensitivity of the National Institutes of Health Stroke Scale for Spontaneous Intracerebral Hemorrhage in an Uncontrolled Setting
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The National Institutes of Health Stroke Scale (NIHSS) is commonly used to measure neurologic function and guide treatment after spontaneous intracerebral hemorrhage (ICH) in routine stroke clinics. We evaluated its reliability and sensitivity to detect change with consecutive and unique rater combinations in a real-world setting. METHODS: Conservative measures of interrater reliability (unweighted Kappa (κ), Intraclass Correlation Coefficient (ICC1,1) and sensitivity to detect change (Minimal Detectable Difference (MDD)) were estimated. Sixty-one repeated ratings were completed within 1 week after ICH by physicians and nurses with no investigator intervention. RESULTS: Reliability (consistency) of the NIHSS total score was good for both physicians vs. nurses and nurses vs. nurses (ICC=0.78, 95%CI: 0.58-0.89 and ICC=0.75, 95%CI: 0.55-0.87 respectively) in this scenario. Reliability (agreement) of items 1C and 9 were excellent (κ>=0.61) for both rater comparisons, however, reliability was poor to fair on most remaining items (κ:0.01-0.60), with item 11 being completely unreliable in this scenario (κ<0.01). The MDD95 of the total NIHSS score was ±10 and ±11 points for physician vs. nurse and nurse vs. nurse comparisons. CONCLUSIONS: The reliability of the NIHSS is good overall for ICH even in an uncontrolled setting. However, on repeated measurements changes in total NIHSS score of at least >=10 points need to be observed for clinicians to be confident that real changes had occurred within 1 week after ICH.
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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 | 0.028 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".