Lung-protective ventilation for the surgical patient: international expert panel-based consensus recommendations
Bibliographic record
Abstract
Postoperative pulmonary complications (PPCs) occur frequently and are associated with substantial morbidity and mortality. Evidence suggests that reduction of PPCs can be accomplished by using lung-protective ventilation strategies intraoperatively, but a consensus on perioperative management has not been established. We sought to determine recommendations for lung protection for the surgical patient at an international consensus development conference. Seven experts produced 24 questions concerning preoperative assessment and intraoperative mechanical ventilation for patients at risk of developing PPCs. Six researchers assessed the literature using questions as a framework for their review. The modified Delphi method was utilised by a team of experts to produce recommendations and statements from study questions. An expert consensus was reached for 22 recommendations and four statements. The following are the highlights: (i) a dedicated score should be used for preoperative pulmonary risk evaluation; and (ii) an individualised mechanical ventilation may improve the mechanics of breathing and respiratory function, and prevent PPCs. The ventilator should initially be set to a tidal volume of 6–8 ml kg −1 predicted body weight and positive end-expiratory pressure (PEEP) 5 cm H 2 O. PEEP should be individualised thereafter. When recruitment manoeuvres are performed, the lowest effective pressure and shortest effective time or fewest number of breaths should be used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".