Comorbidities and Medical Complications in Hospitalized Subarachnoid Hemorrhage Patients
Bibliographic record
Abstract
BACKGROUND: Aneurysmal subarachnoid hemorrhage (SAH) remains a devastating condition with a case fatality of 36% at 30 days. Risk factors for mortality in SAH patients include patient demographics and the severity of the neurological injury. Pre-existing conditions and non-neurological medical complications occurring during the index hospitalization are also risk factors for mortality in SAH. The magnitude of the effect on mortality of pre-existing conditions and medical complications, however, is less well understood. In this study, we aim to determine the effect of pre-existing conditions and medical complications on SAH mortality. METHODS: For a 25% random sample of the Greater Montreal Region, we used discharge abstracts, physician billings, and death certificate records, to identify adult patients with a new diagnosis of non-traumatic SAH who underwent cerebral angiography or surgical clipping of an aneurysm between 1997 and 2014. RESULTS: The one-year mortality rate was 14.76% (94/637). Having ≥3 pre-existing conditions was associated with increased one-year mortality OR 3.74, 95% CI [1.25, 9.57]. Having 2, or ≥3 medical complications was associated with increased one-year mortality OR, 2.42 [95% CI 1.25-4.69] and OR, 2.69 [95% CI 1.43-5.07], respectively. Sepsis, respiratory failure, and cardiac arrhythmias were associated with increased one-year mortality. Having 1, 2, or ≥3 pre-existing conditions was associated with increased odds of having medical complications in hospital. CONCLUSIONS: Pre-existing conditions and in-hospital non-neurological medical complications are associated with increased one-year mortality in SAH. Pre-existing conditions are associated with increased medical complications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".