Abstract P40: Declining Morbidity From Subarachnoid Hemorrhage in the Last 4 Decades: A Pooled Analysis of 13,343 Patients
Bibliographic record
Abstract
Introduction: Subarachnoid hemorrhage (SAH) mortality is decreasing, but data on functional outcomes over time is lacking. Methods: We created trends of good (Glasgow Outcomes Scale [GOS] of 4 or 5) and optimal (GOS of 5) functional outcomes and mortality (GOS of 1) using linear regression in 15 SAH trials and registries from 1982 to 2014. Models adjusted for age, sex, history of hypertension, World Federation of Neurological Surgeons grade, Fisher grade, aneurysm size, location, and repair modality, and whether data was from a clinical trial or registry. Analyses were repeated separately for the clinical trials and registries. Missing data were handled with multiple imputation. Results: Overall, 13,343 SAH patients were included. 9,524 (71%) patients had good functional outcome, while 1,608 (12%) died. There was a 0.6% adjusted improvement (95% confidence interval [CI]: 0.5% to 0.7%; p<0.001) per year in good functional outcome and a 0.1% adjusted reduction (95% CI: -0.2% to -0.08%; p<0.001) per year in mortality. For patients enrolled in clinical trials, there was no change good functional outcomes (0%; 95% CI: -0.2% to 0.1%; p=0.923) or mortality (0.0% change per year; 95% CI: -0.09% to 0.1%; p=0.676). Clinical registry patients experienced a 1.2% improvement (95% CI: 1.0% to 1.4%; p<0.001) in good functional outcome and a 0.3% reduction (95% CI: -0.4% to -0.1%; p<0.001) in mortality. Conclusions: SAH morbidity and mortality decreased from the 1980s to 2010s. This data can be helpful for researchers planning trials, clinicians discussing expected outcomes with patients and family members, and healthcare administrators planning resource utilization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.018 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".