Abstract 207: Hospital Case Volume is Associated With Mortality in Patients Hospitalized With Subarachnoid Hemorrhage
Bibliographic record
Abstract
Background and Purpose: Prior studies have suggested hospital case volume may be associated with improved outcomes after SAH, but contemporary national data are limited. We hypothesized that high volume compared to low volume centers are associated with lower in-hospital mortality. Methods: Using the Get With The Guidelines-Stroke (GWTG-Stroke) registry, we analyzed patients with discharge diagnosis of SAH between April 2003 and March 2012. We assessed the association of annual SAH case volume with in-hospital mortality using multivariable logistic regression adjusting for relevant patient, hospital, and geographic characteristics. Results: Among 29,484 patients with SAH from 644 hospitals, the median annual case volume per hospital was 8.4 (25 th -75 th percentile 6.5-12.6) patients. Mean in-hospital mortality was 25.7% but was lower with increasing annual SAH volume (figure): 29.7% in quartile 1 (4-6.5 per year), 26.8% in quartile 2 (6.5-8.4 per year), 24.0% in quartile 3 (8.4-12.5 per year), and 22.2% om quartile 4 (>12.6 per year). Adjusting for patient (age, sex, race, medical history, stroke unit care, arriving time on or off hours) and hospital characteristics (bed size, location, teaching status, annual stroke volume, annual thrombolytic volume, and primary stroke center status), higher hospital SAH volume was independently associated with lower in-hospital mortality (adjusted odds ratio 0.80, 95% confidence intervals 0.68-0.95, highest quartile SAH volume hospitals compared with lowest quartile volume hospitals). Conclusions: In a large nationwide registry, we observed that patients treated at hospitals with higher volumes of SAH patients have lower in-hospital mortality, independent of patient and other hospital characteristics. Our data suggest that experienced centers may provide more optimized care for these patients.
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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 | 0.005 |
| 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) | 0.003 | 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".