Superiority of sugammadex in preventing postoperative pulmonary complications
Bibliographic record
Abstract
BACKGROUND: Postoperative pulmonary complications often lead to increased mortality and financial burden. Residual paralysis plays a critical role in postoperative pulmonary complications. This meta-analysis was performed to determine whether sugammadex overmatches neostigmine in reducing postoperative pulmonary complications. METHODS: PubMed, Embase, Web of Science, Medline through Ovid, Cochrane Library, Wanfang, China National Knowledge Infrastructure, and Chinese BioMedical Literature Databases were searched from their inception to 24 June, 2021. Random effects models were used for all analyses. Cochrane risk of bias tool was used to assess the quality of RCTs, while Newcastle Ottawa Quality Assessment Scale was used to assess for the quality of cohort studies. RESULTS: Seventeen studies were included in the meta-analysis. Pooled data from cohort studies showed reversing neuromuscular blocking with sugammadex had less risk of compound postoperative pulmonary complications (relative risk [RR]: 0.73; 95% confidence interval [CI]: 0.60-0.89; P = 0.002; I2 = 81%), pneumonia (RR: 0.64; 95% CI: 0.48-0.86; I2 = 42%) and respiratory failure (RR: 0.48; 95% CI: 0.41-0.56; I2 = 0%). However, pooled data from RCTs did not show any difference between the two groups in pneumonia (RR: 0.58; 95% CI: 0.24-1.40; I2 = 0%) and no respiratory failure was reported in the included RCTs. The difference was not found between sugammadex and neostigmine about atelectasis in pooled data from either RCTs (RR: 0.85; 95% CI: 0.69-1.05; I2 = 0%) or cohort studies (RR: 1.01; 95% CI: 0.87-1.18; I2 = 0%). CONCLUSION: The evidence of superiority of sugammadex was limited by the confounding factors in cohort studies and small scale of RCTs. Whether sugammadex precedes neostigmine in preventing pulmonary complications after surgery is still unknown. Well-designed RCTs with large scale are needed. REGISTRATION: PROSPERO ( https://www.crd.york.ac.uk/PROSPERO/ ); CRD 42020191575.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.030 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".