Quality criteria for pediatric oncology centers: A systematic literature review
Bibliographic record
Abstract
INTRODUCTION: Survival of children and adolescents diagnosed with cancer improved over the last decades due to better diagnostics, treatment, and supportive care. Quality criteria that measure, compare, and make the quality of care of individual pediatric oncology centers more transparent are heterogeneous and inconsistent. AIM: With this systematic review, we aimed to summarize existing quality criteria for pediatric oncology centers in countries with highly developed health-care systems. METHODS: We searched three databases for publications, and websites for guidelines about quality criteria for pediatric oncology centers in February 2022. We considered all types of publications except expert opinions. We excluded publications not focusing on highly developed health-care systems, addressing the certification of professionals, or focusing on subspecialties (e.g., pediatric neuro-oncology). We discarded quality criteria if they were too specific (e.g., for a specific treatment center), too broad (e.g., national 5-year overall survival), or if the aspect was covered by standardized clinical procedures or at the national level. We grouped the identified criteria thematically. RESULTS: We identified 18 publications and guideline documents with 530 criteria, of which 201 fulfilled the inclusion criteria. The combination of similar criteria resulted in 90 overarching criteria, which we assigned to the following categories: facilities and networks, multidisciplinary team and other experts, supportive care, treatment, long-term care, and volume and numbers. CONCLUSION: Our results provide a comprehensive overview of existing quality criteria for pediatric oncology in countries with highly developed health-care systems. These criteria can serve as a basis to develop national quality criteria in pediatric oncology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".