Intraoperative open lung condition and postoperative pulmonary complications. A secondary analysis of iPROVE and iPROVE‐O2 trials
Bibliographic record
Abstract
BACKGROUND: The preventive role of an intraoperative recruitment maneuver plus open lung approach (RM + OLA) ventilation on postoperative pulmonary complications (PPC) remains unclear. We aimed at investigating whether an intraoperative open lung condition reduces the risk of developing a composite of PPCs. METHODS: ratio was ≥ or <400 mmHg, respectively. We used a multivariable logistic regression model that included potential confounders selected with directed acyclic graphs (DAG) using Dagitty software built with variables that were considered clinically relevant based on biological mechanism or evidence from previously published data. PPCs included severe acute respiratory failure, acute respiratory distress syndrome, and pneumonia. RESULTS: A total of 1480 patients were included in the final analysis, with 718 (49%) classified as OL. The rate of severe PPCs during the first seven postoperative days was 6.0% (7.9% in the NOL and 4.4% in the OL group, p = .007). OL was independently associated with a lower risk for severe PPCs during the first 7 and 30 postoperative days [odds ratio of 0.58 (95% CI 0.34-0.99, p = .04) and 0.56 (95% CI 0.34-0.94, p = .03), respectively]. CONCLUSIONS: An intraoperative open lung condition was associated with a reduced risk of developing severe PPCs in intermediate-to-high risk patients undergoing abdominal surgery. TRIAL REGISTRATION: Registered at clinicaltrials.gov NCT02158923 (iPROVE), NCT02776046 (iPROVE-O2).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".