Prioritization of Randomized Clinical Trial Questions for Children Hospitalized With Common Conditions
Bibliographic record
Abstract
Importance: There is a lack of randomized clinical trial (RCT) data to guide many routine decisions in the care of children hospitalized for common conditions. A first step in addressing the shortage of RCTs for this population is to identify the most pressing RCT questions for children hospitalized with common conditions. Objective: To identify the most important and feasible RCT questions for children hospitalized with common conditions. Design, Setting, and Participants: For this consensus statement, a 3-stage modified Delphi process was used in a virtual conference series spanning January 1 to September 29, 2022. Forty-six individuals from 30 different institutions participated in the process. Stage 1 involved construction of RCT questions for the 10 most common pediatric conditions leading to hospitalization. Participants used condition-specific guidelines and reviews from a structured literature search to inform their development of RCT questions. During stage 2, RCT questions were refined and scored according to importance. Stage 3 incorporated public comment and feasibility with the prioritization of RCT questions. Main Outcomes and Measures: The main outcome was RCT questions framed in a PICO (population, intervention, control, and outcome) format and ranked according to importance and feasibility; score choices ranged from 1 to 9, with higher scores indicating greater importance and feasibility. Results: Forty-six individuals (38 who shared demographic data; 24 women [63%]) from 30 different institutions participated in our modified Delphi process. Participants included children's hospital (n = 14) and community hospital (n = 13) pediatricians, parents of hospitalized children (n = 4), other clinicians (n = 2), biostatisticians (n = 2), and other researchers (n = 11). The process yielded 62 unique RCT questions, most of which are pragmatic, comparing interventions in widespread use for which definitive effectiveness data are lacking. Overall scores for importance and feasibility of the RCT questions ranged from 1 to 9, with a median of 5 (IQR, 4-7). Six of the top 10 selected questions focused on determining optimal antibiotic regimens for 3 common infections (pneumonia, urinary tract infection, and cellulitis). Conclusions and Relevance: This consensus statementhas identified the most important and feasible RCT questions for children hospitalized with common conditions. This list of RCT questions can guide investigators and funders in conducting impactful trials to improve care and outcomes for hospitalized children.
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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 | 0.018 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".