Retrospective Assessment of Initial Stroke Severity
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The NIH Stroke Scale (NIHSS) and the Canadian Neurological Scale (CNS) have been reported to be useful for the retrospective assessment of initial stroke severity. However, unlike the CNS, the NIHSS requires detailed neurological assessments that may not be reflected in all patient records, potentially limiting its applicability. We assessed the reliability of the retrospective algorithms and the proportions of missing items for the NIHSS and CNS in stroke patients admitted to an academic medical center (AMC) and 2 community hospitals. METHODS: Randomly selected records of patients with ischemic stroke admitted to an AMC (n=20) and community hospitals with (CH1, n=19) and without (CH2, n=20) acute neurological consultative services were reviewed. NIHSS and CNS scores were assigned independently by 2 neurologists using published algorithms. Interrater reliability of the scores was determined with the intraclass correlation coefficient, and the numbers of missing items were tabulated. RESULTS: The intraclass correlation coefficient for NIHSS and CNS, respectively, were 0.93 (95% CI, 0.82 to 1.00) and 0.97 (95% CI, 0.90 to 1.00) for the AMC, 0.89 (95% CI, 0.75 to 1.00) and 0.88 (95%, 0.73 to 1.00) for the CH1, and 0.48 (95% CI, 0.26 to 0.70) and 0.78 (95% CI, 0.60 to 0.96) for the CH2. More NIHSS items were missing at the CH2 (62%) versus the AMC (27%) and the CH1 (23%, P:=0.0001). In comparison, 33%, 0%, and 8% of CNS items were missing from records from CH2, AMC, and CH1, respectively (P:=0.0001). CONCLUSIONS: The levels of interrater agreement were almost perfect for retrospectively assigned NIHSS and CNS scores for patients initially evaluated by a neurologist at both an AMC and a CH. Levels of agreement for the CNS were substantial at a CH2, but interrater agreement for the NIHSS was only moderate in this setting. The proportions of missing items are higher for the NIHSS than the CNS in each setting, particularly limiting its application in the hospital without acute neurological consultative services.
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".