Retrospective NIH Stroke Scale: Not Applicable Without Initial Evaluation by a Neurologist
Bibliographic record
Abstract
P5 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, 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 and 2 community hospitals. Methods: Randomly selected records of patients with ischemic stroke admitted to an academic medical center (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 two neurologists using published algorithms. Inter-rater reliability of the scores was determined with the intraclass correlation coefficient (ICC), and numbers of missing items were tabulated. Results: The ICCs for NIHSS and CNS respectively were 0.93 (95% CI 0.82–1.0) and 0.97 (0.90–1.0) for the AMC, 0.89 (0.75–1.0) and 0.88 (0.73–1.0) for CH1, and 0.48 (0.26–0.70) and 0.78 (0.6–0.96) for CH2. More NIHSS items were missing at CH2 (62%) vs. the AMC (27%) and 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). The ICCs for NIHSS missing items were 0.53 (95% CI 0.31–0.75), 0.7 (0.5–0.9), 0.64 (0.42–0.85), for the 3 hospitals, respectively. Conclusions: There are substantial levels of agreement for retrospectively-assigned NIHSS and CNS scores for patients initially evaluated by a neurologist at both an AMC and a CH. However, the proportions of missing items are higher for the NIHSS in each setting, effectively reducing it to the items captured on the CNS, and particularly limiting the NIHSS application in hospitals 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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.007 | 0.003 |
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".