Anticancer Therapy and Mortality of Adult Patients with Hematologic Malignancy and COVID-19: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) might affect cancer treatment outcomes. This systematic review and meta-analysis identified the prognostic predictors of adult patients with hematologic malignancies and COVID-19, and evaluated the effect of anticancer therapy on mortality. We performed a literature search of electronic databases and identified additional studies from the bibliographies of the articles that were retrieved. Two investigators independently extracted data according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines. We evaluated study quality using the Newcastle–Ottawa Scale and performed a meta-analyses in order to evaluate the effect of anticancer therapy on mortality among adult patients with hematologic malignancies and COVID-19. Heterogeneity was assessed with the I2 statistic. The meta-analysis included 12 studies. The overall mortality rate was 36.3%. The pooled risk difference (RD) in mortality between patients receiving and not receiving anticancer therapy was 0.14 (95% confidence interval [CI]: 0.02–0.26; I2 = 76%). The pooled RD in mortality associated with chemotherapy was 0.22 (95% CI: 0.05–0.39; I2 = 48%), and with immunosuppression was 0.20 (95% CI: 0.05–0.34; I2 = 67%). In the subgroup analyses, anticancer-therapy-associated mortality was higher in females (RD = 0.57; 95% CI: 0.29–0.85; I2 = 0%) than in males (RD = 0.28; 95% CI: 0.04–0.52; I2 = 0%). Among patients with hematologic malignancies and COVID-19, those receiving anticancer therapy had a higher mortality risk, regardless of sex. The mortality risk was higher in females than in males. These results indicate that caution should be exercised when administering anticancer therapy to patients with hematologic malignancies and COVID-19.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.047 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".