Effects of Institutional Caseload of Subarachnoid Hemorrhage on Mortality
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Procedures requiring specific skill sets often have been shown to depend on institutional volume, that is, centers receiving a higher volume observe better outcomes in those patients. This relationship recently has been shown to exist for subarachnoid hemorrhage(SAH) patients in a large study in the United States. We aim to examine this relationship for SAH patients in England, restricting analysis to specialist neurosurgical units. METHODS: Aggregate counts of patients with SAH in 25 specialist neuroscience centers in England, from 2005 to 2011, were obtained from the Hospital Episode Statistics database maintained by the National Health Service Information Center. These data were linked with national mortality statistics to obtain counts of deaths. Poisson regression was used to investigate the relationship between institutional caseload of SAH and 6-month mortality from any cause. Six-month mortality rates and mortality ratios were computed. RESULTS: Annual institutional caseload of admissions with SAH was inversely related to 6-month mortality (P=0.009; r(2)=0.26). Each 100-patient increase in annual patient volume was associated with a 24% reduction in mortality (adjusted mortality ratio, 0.76; confidence interval, 0.67-0.87). This relationship was consistent across the entire range of annual institutional caseloads examined (29-367 cases for the lowest and highest volumes seen in a single center in 1 year). CONCLUSIONS: Our results provide support for management of SAH at high-volume centers and suggest that health care policy in this setting should pursue regionalization while ensuring an adequate geographic spread of access to care.
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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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".