Infarct Timing and Predictors of Infarct-Free Survival in Patients with Aneurysmal Subarachnoid Hemorrhage
Bibliographic record
Abstract
Background/Objectives: Cerebral infarction significantly worsens outcomes after aneurysmal subarachnoid hemorrhage (SAH). This retrospective study analyzed early predictors of infarct-free survival and the impact of infarct timing on clinical outcomes. Methods: We reviewed 988 consecutive SAH patients treated from 2003 to 2016, all with follow-up CT scans. Baseline clinical and SAH characteristics were recorded to identify predictors of infarct-free survival and assess the relationship between infarct timing and outcomes. Results: Cerebral infarctions occurred in 475 patients (48.1%) at a median of 3.4 days post-SAH; 70.9% happened within the first week. Earlier infarctions were associated with higher in-hospital mortality (odds ratio [OR] = 0.91 per day increase, p < 0.0001) and poor 6-month outcomes (modified Rankin Scale > 3; OR = 0.96 per day increase, p = 0.012), especially within 48 h. Independent predictors of infarct-free survival included poor initial condition (WFNS ≥ 4, adjusted hazard ratio [aHR] = 1.82, p < 0.0001), intraventricular hemorrhage (aHR = 1.25, p = 0.041), aneurysm rebleeding (aHR = 1.76, p < 0.0001), acute hydrocephalus (aHR = 1.38, p = 0.020), and daily aspirin intake (aHR = 0.68, p = 0.002). The number of baseline risk factors (0–5) strongly influenced both infarction likelihood and timing (p < 0.0001). Conclusions: Cerebral infarctions predominantly occur within the first week after SAH, with earlier infarctions having a more severe impact on outcomes. Initial risk factor-adapted SAH management may improve functional outcomes.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".