Hospital volume and mortality for mechanical ventilation of medical and surgical patients: A population-based analysis using administrative data*
Bibliographic record
Abstract
OBJECTIVE: In an effort to improve efficiency and quality of care, regionalization of adult critical care services, similar to trauma and neonatal intensive care, has been suggested. However, there is little research to understand if hospitals with higher patient volumes have better outcomes. Our objective is to determine whether hospital volume is associated with improved survival for medical or surgical patients receiving mechanical ventilation. DESIGN: Population-based retrospective cohort study. SETTING: Province of Ontario, Canada. PATIENTS: A total of 13,846 medical and 6,373 surgical patients receiving mechanical ventilation for greater than two consecutive days between 1998 and 2000. INTERVENTIONS: None. MEASUREMENTS: Odds ratio for death within 30 days of initiation of mechanical ventilation was calculated in relation to hospital volume of ventilation. Estimates were adjusted for patient demographics, diagnoses, and urgency status; hospital region and rural location; and accounted for clustering within hospitals. MAIN RESULTS: There was no effect of volume on mortality for surgical patients. After adjustment for clustering, among medical patients, the lowest-volume category (<100 episodes/yr) had a nonsignificant increase in mortality, with an odds ratio (95% confidence interval) of 1.13 (0.87-1.47) compared with the highest-volume category (> or =700 episodes/yr). A post hoc analysis revealed that within the lowest-volume category, the proportion of patients transferred to larger hospitals was 81% for hospitals with <20 episodes/yr and only 32% for hospitals with 20-99 episodes/yr, with odds ratios (95% confidence interval) for mortality of 0.74 (0.49-1.12) and 1.18 (0.90-1.54), respectively, compared with the highest-volume category. CONCLUSIONS: For surgical patients requiring mechanical ventilation for >2 days, hospital volume had no effect on mortality. For medical patients, higher mortality may occur in a subgroup of low-volume hospitals that do not routinely transfer their patients to larger-volume facilities. This finding needs further investigation in a larger-sized study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".