Optimal outcome measures for a trial of not routinely measuring gastric residual volume in neonatal care: a mixed methods consensus process
Bibliographic record
Abstract
BACKGROUND: Routine measurement of gastric residual volume to guide feeding is widespread in neonatal units but not supported by high-quality evidence. Outcome selection is critical to trial design. OBJECTIVE: To determine optimal outcome measures for a trial of not routinely measuring gastric residual volume in neonatal care. DESIGN: A focused literature review, parent interviews, modified two-round Delphi survey and stakeholder consensus meeting. PARTICIPANTS: Sixty-one neonatal healthcare professionals participated in an eDelphi survey; 17 parents were interviewed. 19 parents and neonatal healthcare professionals took part in the consensus meeting. RESULTS: Literature review generated 14 outcomes, and parent interviews contributed eight additional outcomes; these 22 outcomes were then ranked by 74 healthcare professionals in the first Delphi round where four further outcomes were proposed; 26 outcomes were ranked in the second round by 61 healthcare professionals. Five outcomes were categorised as 'consensus in', and no outcomes were voted 'consensus out'. 'No consensus' outcomes were discussed and voted on in a face-to-face meeting by 19 participants, where four were voted 'consensus in'. The final nine consensus outcomes were: mortality, necrotising enterocolitis, time to full enteral feeds, duration of parenteral nutrition, time feeds stopped per 24 hours, healthcare-associated infection; catheter-associated bloodstream infection, change in weight between birth and neonatal discharge and pneumonia due to milk aspiration. CONCLUSIONS AND RELEVANCE: We have identified outcomes for a trial of no routine measurement of gastric residual volume to guide feeding in neonatal care. This outcome set will ensure outcomes are important to healthcare professionals and parents.
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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 | 0.748 | 0.777 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".