Early Motor Glasgow Coma Scale Predicts Unfavorable Functional Outcome after Poor-Grade Subarachnoid Hemorrhage
Bibliographic record
Abstract
ABSTRACT Background: We assessed whether the motor component of the Glasgow Coma Scale (GCSm) is independently associated with unfavorable outcomes in aggressively treated poor-grade subarachnoid hemorrhage (SAH) patients. Methods: Retrospective cohort of poor-grade SAH patients (World Federation of Neurosurgical Societies (WFNS) grades IV and V). The best GCSm score achieved within 24 h of admission was stratified into four categories (<4, 4, 5 or 6). Outcomes were classified as favorable [modified Rankin Scale (mRS) ≤ 2] or unfavorable (mRS ≥ 3). Multivariable logistic regression was performed to identify independent predictors of unfavorable outcome. Results: A total of 179 patients were admitted during the study period (mean age 55.9 ± 12.1; 68.2% female). Thirty-three patients (33/179 – 18%) died before aneurysm treatment, one patient had missing GCSm data at 24 h and sixteen patients (16/179; 9%) were lost to follow-up. One hundred and twenty-nine patients (129/179 – 72%) were included in the final analysis. No patient with GCSm < 4 had a favorable outcome (sensitivity 22.4%, specificity 100%, positive predictive value 100% and negative predictive value 67.8% for unfavorable outcome). Delayed cerebral ischemia-related cerebral infarction (odds ratio (OR) 4.06; 1.56−11.11 95% CI, p = 0.004) and the best GCSm score were independently associated with unfavorable outcome. There was a stepwise decrease in the rate of unfavorable outcome from GCSm < 4 to GCSm = 6 (<4 = 100%; 4 = 80%; 5 = 46% and 6 = 20%). Each one-point decrease in GCSm score was associated with an OR of 3.52 (1.77−7.92 95% CI, p = < 0.001) for unfavorable outcome. Conclusion: The GCSm score was independently associated with unfavorable outcome. All patients with a GCSm score < 4 experienced an unfavorable outcome.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".