Abstract 20: Relationship Between Physician Annual Volume And Stroke Mortality - Results From the Ontario Stroke Registry
Bibliographic record
Abstract
Objective: Previous studies have demonstrated an inverse relationship between hospital volume and stroke mortality. However, little is known about the effect of physician volume on stroke mortality. Methods: We used the Ontario Stroke Registry (OSR) to identify a cohort of stroke/TIA patients admitted to 11 regional stroke centers in Ontario, Canada (Jul 2003 - Mar 2008). The most responsible admitting physician was identified by linking the OSR data to the Discharge Abstract Database maintained by the Canadian Institute for Health Information. The risk of death after discharge was determined through linkages to the Ontario Registered Persons Database. Patients were divided into quintiles based on the annual volumes of the most responsible physicians. Multivariable analyses of physician volume effect were conducted using random effects hierarchical logistic regression models, adjusting for patient characteristics (sex, age, severity, stroke type, comorbidity and in-hospital care), physician characteristic (physician type, annual volume) and hospital characteristics (annual volume). Risk-adjusted mortality rates were calculated to determine if there was a threshold effect for physician volume. Results: There were 14,285 admitted stroke/TIA patients who were treated by 783 physicians. Physician volume had an inverse relationship with in-hospital mortality and 30-day mortality (p<0.0001). Risk-adjusted rates are shown in Figure. Conclusion: Higher physician annual volume is associated with lower stroke mortality at the regional stroke centers (average volume: 180~370 patients/year). A minimum annual volume of seeing stroke patients by admitting physicians should be recommended. Shifting the care of more stroke patients to high-volume physicians could potentially reduce mortality after stroke.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".