Systematic Review of Studies Investigating Hospital Readmissions in Pediatric Oncology
Bibliographic record
Abstract
PURPOSE: This systematic review aimed to identify and synthesize evidence on hospital readmissions among pediatric oncology patients, focusing on the indications, risk factors, and proposed strategies to prevent readmissions. METHOD: The review followed PRISMA 2020 guidelines. Databases including Embase, Medline, Scopus, Mendeley, and Google Scholar were searched. Studies examining hospital readmission as a main outcome in pediatric cancer patients, including those undergoing surgical oncology procedures or receiving hematopoietic cell transplantation, were included. Quality assessment was undertaken using the Newcastle-Ottawa Scale and PROBAST tool. RESULTS: A total of 18 studies met the inclusion criteria. The studies spanned from 2008 to 2023, with an increase in publications from 2020 onward (61%). Fever and infection were the most common readmission indications. Statistically significant risk factors reported included younger age, specific cancer types (e.g., acute lymphoblastic leukemia), and socioeconomic factors. Prevention strategies proposed included early follow-up, tailored anticipatory guidance, discharge education, and specialized care at pediatric oncology centers. CONCLUSIONS: This systematic review highlights the multifaceted nature of hospital readmissions in pediatric oncology patients and the need for standardized definitions, additional studies, and comprehensive interventions. Future research should focus on high-quality prospective studies, integration of predictive analytics, and addressing socioeconomic disparities to improve patient outcomes.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".