Procedure volume is one determinant of centre effect in mechanically ventilated patients
Bibliographic record
Abstract
Survival rates vary significantly between intensive care units, most notably in patients requiring mechanical ventilation (MV). The present study sought to estimate the effect of hospital MV volume on hospital mortality. We included 179,197 consecutive patients who received mechanical ventilation in 294 hospitals. Multivariate logistic regression models with random intercepts were used to estimate the effect of annual MV volume in each hospital, adjusting for differences in severity of illness and case mix. Median annual MV volume was 162 patients (interquartile range 99-282). Hospital mortality in MV patients was 31.4% overall, 40.8% in the lowest annual volume quartile and 28.2% in the highest quartile. After adjustment for severity of illness, age, diagnosis and organ failure, higher MV volume was associated with significantly lower hospital mortality among MV patients (OR 0.9985 per 10 additional patients, 95% CI 0.9978-0.9992; p = 0.0001). A significant centre effect on hospital mortality persisted after adjustment for volume effect (p < 0.0001). Our study demonstrated higher hospital MV volume to be independently associated with increased survival among MV patients. Significant differences in outcomes persisted between centres after adjustment for hospital MV volume, supporting a role for other significant determinants of the centre effect.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".