Impact of premorbid hypertension on haemorrhage severity and aneurysm rebleeding risk after subarachnoid haemorrhage
Bibliographic record
Abstract
OBJECTIVE: Arterial hypertension (HTN) is a risk factor for subarachnoid haemorrhage (SAH). We aimed to assess the impact of premorbid HTN on the severity of initial bleeding and the risk of aneurysm rebleeding after SAH. DESIGN: Retrospective analysis of a prospective cohort study of all SAH patients admitted to Columbia University Medical Center between 1996 and 2012. RESULTS: We enrolled 1312 consecutive patients with SAH; 643 (49%) had premorbid HTN. Patients with premorbid HTN presented more frequently as Hunt-Hess Grade IV or V (36% vs 25%, p<0.001) and World Federation of Neurosurgical Societies (WFNS) Grade 4 or 5 (42.6% vs 28.2%, p<0.001), with larger amounts of subarachnoid (Hijdra Sum Score 17 vs 14, p<0.001) and intraventricular blood (median IVH sum score 2 vs 1, p<0.001), and more often with intracerebral haemorrhage (20% vs 13%, p=0.002). In multivariate analysis, patients with premorbid HTN had a higher risk of in-hospital aneurysm rebleeding (11.8% vs 5.5%, adjusted OR 1.67, 95% CI 1.02 to 2.74, p=0.04) after adjusting for age, admission, Hunt-Hess grade, size and site of the ruptured aneurysm. CONCLUSIONS: Premorbid HTN is associated with increased severity of the initial bleeding event and represents a significant risk factor for aneurysm rebleeding. Given that aneurysm rebleeding is a potentially fatal-but preventable-complication, these findings are of clinical relevance.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".