The association of postoperative pulmonary complications in 109,360 patients with pressure‐controlled or volume‐controlled ventilation
Bibliographic record
Abstract
Summary We thought that the rate of postoperative pulmonary complications might be higher after pressure‐controlled ventilation than after volume‐controlled ventilation. We analysed peri‐operative data recorded for 109,360 adults, whose lungs were mechanically ventilated during surgery at three hospitals in Massachusetts, USA. We used multivariable regression and propensity score matching. Postoperative pulmonary complications were more common after pressure‐controlled ventilation, odds ratio (95%CI) 1.29 (1.21–1.37), p < 0.001. Tidal volumes and driving pressures were more varied with pressure‐controlled ventilation compared with volume‐controlled ventilation: mean (SD) variance from the median 1.61 (1.36) ml.kg −1 vs. 1.23 (1.11) ml.kg −1 , p < 0.001; and 3.91 (3.47) cmH 2 O vs. 3.40 (2.69) cmH 2 O, p < 0.001. The odds ratio (95%CI) of pulmonary complications after pressure‐controlled ventilation compared with volume‐controlled ventilation at positive end‐expiratory pressures < 5 cmH 2 O was 1.40 (1.26–1.55) and 1.20 (1.11–1.31) when ≥ 5 cmH 2 O, both p < 0.001, a relative risk ratio of 1.17 (1.03–1.33), p = 0.023. The odds ratio (95%CI) of pulmonary complications after pressure‐controlled ventilation compared with volume‐controlled ventilation at driving pressures of < 19 cmH 2 O was 1.37 (1.27–1.48), p < 0.001, and 1.16 (1.04–1.30) when ≥ 19 cmH 2 O, p = 0.011, a relative risk ratio of 1.18 (1.07–1.30), p = 0.016. Our data support volume‐controlled ventilation during surgery, particularly for patients more likely to suffer postoperative pulmonary complications.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".