The Outcome of Gynecologic Cancer Patients With Covid-19 Infection: A Systematic Review And Meta-Analysis
Bibliographic record
Abstract
Abstract Objective Cancer is a comorbidity that leads to progressive worsening of Covid-19 with increased mortality. This is a systematic review and meta-analysis to yield evidence of adverse outcomes of Covid-19 in gynecologic cancer. Methods Searches through PubMed, Google Scholar, ScienceDirect, and medRxiv to find articles on the outcome of gynecologic cancer with Covid-19 (24 July 2021-19 February 2022). Newcastle-Ottawa Scale tool is used to evaluate the quality of included studies. Pooled odds ratio (OR), 95% confidence interval (CI), random-effects model were presented. This study was registered to PROSPERO (CRD42021256557). Results We accepted 51 studies (1991 gynecologic cancer with Covid-19). Covid-19 infection was lower in gynecologic cancer vs hematologic cancer (OR 0.71, CI 0.56-0.90, p 0.005). Severe Covid and death were lower in gynecologic cancer vs lung and hematologic cancer (OR 0.36, CI 0.16-0.80, p 0.01), (OR 0.52, CI 0.44-0.62, p <0.0001), (OR 0.26, CI 0.10-0.67 p 0.005), (OR 0.63, CI 0.47-0.83, p 0.001) respectively. Increased Covid death is seen in gynecologic cancer vs breast, non-covid cancer, and non-cancer covid (OR 1.50, CI 1.20-1.88, p 0.0004), (OR 11.83, CI 8.20-17.07, p <0.0001), (OR 2.98, CI 2.23-3.98, p <0.0001) respectively. Conclusion Gynecologic cancer has higher Covid-19 adverse outcomes compared to non-cancer, breast cancer, non-metastatic, and Covid-19 negative population. Gynecologic cancer has fewer Covid-19 adverse outcomes compared to other cancer types, lung cancer, and hematologic cancer. These findings may aid health policies and services during the ongoing global pandemic.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.042 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".