Prophylactic Noninvasive Ventilation Versus Conventional Care in Patients After Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Cardiac surgery can be accompanied by postoperative complications, which are associated with increased postoperative morbidity and mortality. Therefore, it is necessary to investigate the effect of prophylactic noninvasive ventilation (NIV) after extubation versus conventional pulmonary care on complications after cardiac surgery. MATERIALS AND METHODS: An electronic search of PubMed, Cochrane Library, Ovid, and EMBASE was conducted to find randomized controlled trials which compared the effect of prophylactic NIV with controlled strategies on complications and which were published before April 2018. RESULTS: Ten studies (1011 patients) were included in the final analysis. The atelectasis rate was 32.6% in the prophylactic-NIV group, which was lower than that in the control group (48.71%). Prophylactic NIV could lower the rate of atelectasis, reintubation, and other respiratory complications (pleural effusion, pneumonia, and hypoxia) (odds ratio = 0.43, 0.33, and 0.45; 95% confidence interval: 0.21-0.88, 0. 13-0.84, 0.27-0.75; P = 0.02, 0.02, and 0.002, respectively). The effect on cardiac and distal organ complications (P = 0.07) and hospital mortality (P = 0.62) might be limited. CONCLUSIONS: Prophylactic NIV is associated with a lower rate of postoperative pulmonary complications. The effect on the other complications and hospital mortality might be limited. Further evidence with randomized controlled trials can discern the benefits.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".