Findings Predictive of Poor Outcome in Grade 5 Subarachnoid Hemorrhage: A Cohort Study
Bibliographic record
Abstract
BACKGROUND: Most patients with World Federation of Neurological Surgeons (WFNS) grade 5 subarachnoid hemorrhage (SAH) have poor outcomes. Accurate assessment of prognosis is important for treatment decisions and conversations with families regarding goals of care. Unjustified pessimism may lead to "self-fulfilling prophecy," where withdrawal of life-sustaining measures (WLSM) is invariably followed by death. METHODS: We performed a cohort study involving consecutive patients with WFNS grade 5 SAH to identify variables with >= 90% and >= 95% positive predictive value (PPV) for poor outcome (1-year modified Rankin Score >= 4), as well as findings predictive of WLSM. RESULTS: Of 140 patients, 38 (27%) had favorable outcomes. Predictors with >= 95% PPV for poor outcome included unconfounded 72-hour Glasgow Coma Scale motor score <= 4, absence of >= 1 pupillary light reflex (PLR) at 24 hours, and intraventricular hemorrhage (IVH) score of >= 20 (volume >= 54.6 ml). Intracerebral hemorrhage (ICH) volume >= 53 ml had PPV of 92%. Variables associated with WLSM decisions included a poor motor score (p < 0.0001) and radiographic evidence of infarction (p = 0.02). CONCLUSIONS: We identified several early predictors with high PPV for poor outcome. Of these, lack of improvement in motor score during the initial 72 hours had the greatest potential for confounding from "self-fulfilling prophecy." Absence of PLR at 24 hours, IVH score >= 20, and ICH volume >= 53 ml predicted poor outcome without a statistically significant effect on WLSM decisions. More research is needed to validate prognostic variables in grade 5 SAH, especially among patients who do not undergo WLSM.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".