Determining Optimal Outcome Measures in a Trial Investigating No Routine Gastric Residual Volume Measurement in Critically Ill Children
Bibliographic record
Abstract
BACKGROUND: Choosing trial outcome measures is important. When outcomes are not clinically relevant or important to parents/patients, trial evidence is less likely to be implemented into practice. This study aimed to determine optimal outcome measures for a trial of no routine gastric residual volume (GRV) measurement in critically ill children. METHODS: A mixed-methods approach was used: a focused literature review, parent and clinician interviews, a modified 2-round Delphi, and a stakeholder consensus meeting. RESULTS: The review generated 13 outcomes. Fourteen pediatric intensive care unit (PICU) parents proposed 3 additional outcomes; these 16 were then rated by 28 clinicians in Delphi round 1. Six further outcomes were proposed, and 22 outcomes were rated in the second round. No items were voted "consensus out." The 18 "no-consensus" items were voted in a face-to-face meeting by 30 participants. The final 12 outcome measures were time to reach energy targets, ventilator-associated pneumonia, vomiting, time enteral feeds withheld per 24 hours, necrotizing enterocolitis, length of invasive ventilation, PICU length of stay, mortality, change in weight and markers of feed intolerance (parenteral nutrition administered), feed formula altered, and change to postpyloric feeds all secondary to feed intolerance. CONCLUSION: We have identified 12 outcomes for a trial of no GRV measurement through a multistage process, seeking views of parents and clinicians.
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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 | 0.324 | 0.380 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".