Meta-analysis of mortality factors after COVID-19 infection in pediatric oncology patients
Bibliographic record
Abstract
Objective: There are few clinical studies related to COVID-19 in pediatric cancer patients, and systematic reviews or meta-analyses on its mortality risk factors are particularly lacking. Therefore, we conducted this meta-analysis to systematically analyze the mortality risk factors of pediatric cancer patients after COVID-19 infection, providing effective evidence-based medical evidence for epidemic prevention and control and clinical treatment of pediatric COVID-19 patients. Methods: Electronic databases of PubMed, Embase, Cochrane Library and Web of Science were searched using "cancer" "COVID-19" "children" "mortality" related subject headings and keywords. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of the included studies. Outcomes included age, weight, clinical complications in patients, cancer type, consolidation of cancer treatment, and critical illness. The quality of observational studies was assessed using the Newcastle-Ottawa Scale, which includes criteria such as study population selection, comparability, and evaluation of exposure or outcome, by two independent reviewers. Results: A computerized search of the literature yielded six observational studies with a total of 2,696 patients, and a pooled assessment of predictive factors revealed that the occurrence of adverse clinical complications, the presence of solid tumors, and the presence of acute and critical conditions significantly increased mortality in pediatric oncology patients (P < 0.05), although, overall, aggressive consolidation of cancer treatment significantly reduced the death of patients. Although overall,being in the cancer consolidation treatment period is significantly associated with a reduced risk of patient mortality, there is still an increase in mortality with Radiotherapy, possibly due to immunocompromise (P < 0.05), whereas Immunotherapy and Surgery do not affect patient prognosis. Subgroup analyses showed that prolonged consolidation of cancer treatment reduced mortality. The sensitivity analysis of the results of the outcome indicators was stable with low sensitivity and high confidence. Conclusion: Adverse clinical complications, the presence of solid tumors, and the occurrence of critical conditions increase mortality in pediatric cancer patients. Receiving aggressive cancer treatment is associated with lower mortality rates, but this association should be interpreted with caution, as it may be confounded by other factors. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD420250570932.
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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 | 0.022 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.066 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".