Incidence and Outcome of Aneurysmal Subarachnoid Hemorrhage
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The purpose of this study was to assess nationwide incidence and outcomes of aneurysmal subarachnoid hemorrhage (aSAH). The Swiss SOS (Swiss Study on Subarachnoid Hemorrhage) was established in 2008 and offers the unique opportunity to provide this data from the point of care on a nationwide level. METHODS: All patients with confirmed aneurysmal subarachnoid hemorrhage admitted between January 1, 2009 and December 31, 2014, within Switzerland were recorded in a prospective registry. Incidence rates were calculated based on time-matched population data. Admission parameters and outcomes at discharge and at 1 year were recorded. RESULTS: We recorded data of 1787 consecutive patients. The incidence of aneurysmal subarachnoid hemorrhage in Switzerland was 3.7 per 100 000 persons/y. The number of female patients was 1170 (65.5%). With a follow-up rate of 91.3% at 1 year, 1042 patients (58.8%) led an independent life according to the modified Rankin Scale (0-2). About 1 in 10 patients survived in a dependent state (modified Rankin Scale, 3-5; n=185; 10.4%). Case fatality was 20.1% (n=356) at discharge and 22.1% (n=391) after 1 year. CONCLUSIONS: The current incidence of aneurysmal subarachnoid hemorrhage in Switzerland is lower than expected and an indication of a global trend toward decreasing admissions for ruptured intracranial aneurysms. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT03245866.
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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".