Weaning from mechanical ventilation in the operating room: a systematic review
Bibliographic record
Abstract
Background Postoperative pulmonary complications (PPCs) are associated with postoperative mortality and prolonged hospital stay. Although intraoperative mechanical ventilation (MV) is a risk factor for PPCs, strategies addressing weaning from MV are understudied. In this systematic review, we evaluated weaning strategies and their effects on postoperative pulmonary outcomes. Methods Our protocol was registered on PROSPERO (CRD42022379145). Eligible studies included randomised controlled trials and observational studies of adults weaned from MV in the operating room. Primary outcomes included atelectasis and oxygenation; secondary outcomes included lung volume changes and PPCs. Risk of bias was assessed using the Cochrane Risk of Bias (RoB2) tool, and quality of evidence with the GRADE framework. Results Screening identified 14 randomised controlled trials including 1719 patients; seven studies were limited to the weaning phase and seven included interventions not restricted to the weaning phase. Strategies combining pressure support ventilation (PSV) with positive end-expiratory pressure (PEEP) and low fraction of inspired oxygen (FiO 2 ) improved atelectasis, oxygenation, and lung volumes. Low FiO 2 improved atelectasis and oxygenation but might not improve lung volumes. A fixed-PEEP strategy led to no improvement in oxygenation or atelectasis; however, individualised PEEP with low FiO 2 improved oxygenation and might be associated with reduced PPCs. Half of included studies are of moderate or high risk of bias; the overall quality of evidence is low. Conclusions There is limited research evaluating weaning from intraoperative MV. Based on low-quality evidence, PSV, individualised PEEP, and low FiO 2 may be associated with reduced postoperative pulmonary outcomes. Systematic Review Protocol PROSPERO (CRD42022379145).
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".