Quality and capacity indicators for hospitalized pediatric oncology patients with critical illness: A modified delphi consensus
Bibliographic record
Abstract
BACKGROUND: Hospitalized pediatric hematology-oncology (PHO) patients are at high risk for critical illness, especially in resource-limited settings. Unfortunately, there are no established quality indicators to guide institutional improvement for these patients. The objective of this study was to identify quality indicators to include in PROACTIVE (PediatRic Oncology cApaCity assessment Tool for IntensiVe carE), an assessment tool to evaluate the capacity and quality of pediatric critical care services offered to PHO patients. METHODS: A comprehensive literature review identified relevant indicators in the areas of structure, performance, and outcomes. An international focus group sorted potential indicators using the framework of domains and subdomains. A modified, three-round Delphi was conducted among 36 international experts with diverse experience in PHO and critical care in high-resource and resource-limited settings. Quality indicators were ranked on relevance and actionability via electronically distributed surveys. RESULTS: PROACTIVE contains 119 indicators among eight domains and 22 subdomains, with high-median importance (≥7) in both relevance and actionability, and ≥80% evaluator agreement. The top five indicators were: (a) A designated PICU area; (b) Availability of a pediatric intensivist; (c) A PHO physician as part of the primary team caring for critically ill PHO patients; (d) Trained nursing staff in pediatric critical care; and (e) Timely PICU transfer of hospitalized PHO patients requiring escalation of care. CONCLUSIONS: PROACTIVE is a consensus-derived tool to assess the capacity and quality of pediatric onco-critical care in resource-limited settings. Future endeavors include validation of PROACTIVE by correlating the proposed indicators to clinical outcomes and its implementation to identify service delivery gaps amenable to improvement.
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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 | 0.148 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| 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".