Association of COVID-19 and Mortality in Surgical Populations by Surgery Types: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Since the start of the COVID-19 pandemic thousands of operations have been canceled or postponed in patients with COVID-19. To date no systematic review or meta-analysis has comprehensively estimated the risk of mortality from surgery by surgery type. We aim to delineate the risk of mortality in patients with COVID-19 based on surgery type. Methods: PubMed (MEDLINE), Scopus, OVID the World Health Organization Global Literature on Coronavirus Disease, and Corona-Central databases were searched from December 2019 through January 2022. A total of 4023 studies were identified from databases and through cited references. Studies providing data on mortality in patients undergoing surgery was included. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines for abstracting data were followed and performed independently by 2 reviewers. Quality was assessed using the Newcastle-Ottawa Scale for cohort studies. The main outcome was Mortality in Patients with COVID-19. Results: From a total of 4,023 studies identified, 46 studies with 80,015 patients met our inclusion criteria. The mean age was 67 years, 57% were male. Surgery types included general surgery (14.9%), orthopaedic surgery (23.4%), vascular (6.4%), thoracic (10.6%), urological (8.5%). Patients undergoing surgery with COVID-19 elicited a 9 -fold increased risk of mortality (odds ratio [OR]: 8.99, 95% CI 4.96- 16.32) over those without COVID-19. Urologic surgeries presented the highest risk of mortality (OR:21.62 95% CI 4.53-103.11), orthopaedic and vascular surgery types also had high rates of mortality. General surgery reported the lowest rate of mortality in patients with COVID-19 (OR 3.84, 95% CI 1.92-7.68). Conclusion: Mortality risk in surgical patients with COVID-19 is operation-type specific.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.036 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".