Pulmonary Complications and 30-Day Mortality Rate in COVID-19 Patients Undergoing Surgery
Bibliographic record
Abstract
Hundreds of surgeries are postponed every day during the global COVID -19 pandemic. The hospital and clinicians are in dilemma scheduling elective procedures during the pandemic. The current study was designed to evaluate postoperative pulmonary complications and mortality in COVID-19 patients in a systematic review and meta-analysis of globally published peer-reviewed literatures. A systematic literature search was conducted using the selection criteria in five databases. A quality assessment was made with a validated Newcastle-Ottawa Scale. The meta-analysis worked as a generic inverse variance meta-analysis. A total of 308 articles were identified from different databases and 5 articles with a total 1408 participants were selected for evaluation after successive screenings. The meta-analysis revealed a high global rate of postoperative mortality among COVID-19 patients, as high as 23% (95% CI: 15 to 26), and high postoperative pulmonary complications including pneumonia and acute respiratory distress syndrome. The 30-days mortality rate and prevalence of pulmonary complications were high. There was one death for every five COVID-19 patients undergoing surgical procedures, indicating the need for mitigating strategies to decrease perioperative mortality, transmission to healthcare workers, and non-COVID-19 patients. Larger samples and/or multicenter trials are needed to explore the perioperative mortality dan morbidity rate of patients with COVID-19 undergoing surgeries, and in particular, factors with the highest impact on perioperative mortality. There should be a clinical guideline to determine when to operate or not to operate on patients with COVID-19 for elective and emergency surgeries.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.027 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".