Clinical characteristics and outcome of aneurysmal subarachnoid hemorrhage with intracerebral hematoma
Bibliographic record
Abstract
OBJECTIVE Intracerebral hematoma (ICH) with subarachnoid hemorrhage (SAH) indicates a unique feature of intracranial aneurysm rupture since the aneurysm is in the subarachnoid space and separated from the brain by pia mater. Broad consensus is lacking regarding the concept that ultra-early treatment improves outcome. The aim of this study is to determine the associative factors for ICH, ascertain the prognostic value of ICH, and investigate how the timing of treatment relates to the outcome of SAH with concurrent ICH. METHODS The study data were pooled from the SAH International Trialists repository. Logistic regression was applied to study the associations of clinical and aneurysm characteristics with ICH. Proportional odds models and dominance analysis were applied to study the effect of ICH on 3-month outcome (Glasgow Outcome Scale) and investigate the effect of time from ictus to treatment on outcome. RESULTS Of the 5362 SAH patients analyzed, 1120 (21%) had concurrent ICH. In order of importance, neurological status, aneurysm location, aneurysm size, and patient ethnicity were significantly associated with ICH. Patients with ICH experienced poorer outcome than those without ICH (OR 1.58; 95% CI 1.37-1.82). Treatment within 6 hours of SAH was associated with poorer outcome than treatment thereafter (adjusted OR 1.67; 95% CI 1.04-2.69). Subgroup analysis with adjustment for ICH volume, location, and midline shift resulted in no association between time from ictus to treatment and outcome (OR 0.99; 95% CI 0.94-1.07). CONCLUSIONS The most important associative factor for ICH is neurological status on admission. The finding regarding the value of ultra-early treatment suggests the need to more robustly reevaluate the concept that hematoma evacuation of an ICH and repair of a ruptured aneurysm within 6 hours of ictus is the most optimal treatment path.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".