Outcome of Gynecologic Cancer Patients With The Covid-19 Infection : A Systematic Review And Meta Analysis
Bibliographic record
Abstract
Abstract Objective Cancer is comorbidity, which can lead to progressive worsening of Covid-19 with increased mortality. This is a systematic review and meta-analysis to get 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 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 49 studies with (1994 gynecologic cancer with Covid-19). Covid-19 infection was lower in gynecologic cancer vs hematologic cancer (OR 0.71, CI 0.56–0.89, p 0.003). 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 0.26, CI 0.10–0.67 p 0.005), (OR 0.52, CI 0.43–0.63, p < 0.0001), (OR 0.65, CI 0.49–0.87, p 0.003) respectively. Increased Covid death is seen in gynecologic cancer vs breast, non-covid cancer, and non-cancer covid (OR 1.51, CI 1.20–1.90, p 0.0004), (OR 12.21, CI 8.39–17.77, p < 0.0001), (OR 3.06, CI 2.32–4.04, p < 0.0001) respectively. Conclusion Gynecologic cancer had increased Covid-19 adverse outcomes compared to non-cancer, breast cancer, non-metastatic, and Covid-19 negative population. Gynecologic cancer had lowered Covid-19 adverse outcomes compared to other cancer types, lung cancer, and hematologic cancer. Lack of age and comorbidities stratification due to limited data were limitations. 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 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.043 |
| Bibliometrics | 0.007 | 0.008 |
| 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".