Cluster randomized trial to evaluate the impact of team training on surgical outcomes
Bibliographic record
Abstract
BACKGROUND: The application of safety principles from the aviation industry to the operating room has offered hope in reducing surgical complications. This study aimed to assess the impact on major surgical complications of adding an aviation-based team training programme after checklist implementation. METHODS: A prospective parallel-group cluster trial was undertaken between September 2011 and March 2013. Operating room teams from 31 hospitals were assigned randomly to participate in a team training programme focused on major concepts of crew resource management and checklist utilization. The primary outcome measure was the occurrence of any major adverse event, including death, during the hospital stay within the first 30 days after surgery. Using a difference-in-difference approach, the ratio of the odds ratios (ROR) was estimated to compare changes in surgical outcomes between intervention and control hospitals. RESULTS: Some 22 779 patients were enrolled, including 5934 before and 16 845 after team training implementation. The risk of major adverse events fell from 8·8 to 5·5 per cent in 16 intervention hospitals (adjusted odds ratio 0·57, 95 per cent c.i. 0·48 to 0·68; P < 0·001) and from 7·9 to 5·4 per cent in 15 control hospitals (odds ratio 0·64, 0·50 to 0·81; P < 0·001), resulting in the absence of difference between arms (ROR 0·90, 95 per cent c.i. 0·67 to 1·21; P = 0·474). Outcome trends revealed significant improvements among ten institutions, equally distributed across intervention and control hospitals. CONCLUSION: Surgical outcomes improved substantially, with no difference between trial arms. Successful implementation of an aviation-based team training programme appears to require modification and adaptation of its principles in the context of the the surgical milieu. Registration number: NCT01384474 (http://www.clinicaltrials.gov).
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.000 | 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".