Hospital volume and patient outcomes in pulmonary embolism
Bibliographic record
Abstract
BACKGROUND: In numerous high-risk medical and surgical conditions, a greater volume of patients undergoing treatment in a given setting or facility is associated with better survival. For patients with pulmonary embolism, the relation between the number of patients treated in a hospital (volume) and patient outcome is unknown. METHODS: We studied discharge records from 186 acute care hospitals in Pennsylvania for a total of 15 531 patients for whom the primary diagnosis was pulmonary embolism. The study outcomes were all-cause mortality in hospital and within 30 days after presentation for pulmonary embolism and the length of hospital stay. We used logistic models to study the association between hospital volume and 30-day mortality and discrete survival models to study the association between in-hospital mortality and time to hospital discharge. RESULTS: The median annual hospital volume for pulmonary embolism was 20 patients (interquartile range 10-42). Overall in-hospital mortality was 6.0%, whereas 30-day mortality was 9.3%. In multivariable analysis, very-high-volume hospitals (> or = 42 cases per year) had a significantly lower odds of in-hospital death (odds ratio [OR] 0.71, 95% confidence interval [CI] 0.51-0.99) and of 30-day death (OR 0.71, 95% CI 0.54-0.92) than very-low-volume hospitals (< 10 cases per year). Although patients in the very-high-volume hospitals had a slightly longer length of stay than those in the very-low-volume hospitals (mean difference 0.7 days), there was no association between volume and length of stay. INTERPRETATION: In hospitals with a high volume of cases, pulmonary embolism was associated with lower short-term mortality. Further research is required to determine the causes of the relation between volume and outcome for patients with pulmonary embolism.
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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 | 0.017 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".