Economic evaluation alongside the Speed of Increasing milk Feeds Trial (SIFT)
Bibliographic record
Abstract
OBJECTIVE: To evaluate the cost-effectiveness of two rates of enteral feed advancement (18 vs 30 mL/kg/day) in very preterm and very low birth weight infants. DESIGN: Within-trial economic evaluation alongside a multicentre, two-arm parallel group, randomised controlled trial (Speed of Increasing milk Feeds Trial). SETTING: 55 UK neonatal units from May 2013 to June 2015. PATIENTS: Infants born <32 weeks' gestation or <1500 g, receiving less than 30 mL/kg/day of milk at trial enrolment. Infants with a known severe congenital anomaly, no realistic chance of survival, or unlikely to be traceable for follow-up, were ineligible. INTERVENTIONS: When clinicians were ready to start advancing feed volumes, infants were randomised to receive daily increments in feed volume of 30 mL/kg (intervention) or 18 mL/kg (control). MAIN OUTCOME MEASURE: Cost per additional survivor without moderate to severe neurodevelopmental disability at 24 months of age corrected for prematurity. RESULTS: Average costs per infant were slightly higher for faster feeds compared with slower feeds (mean difference £267, 95% CI -6928 to 8117). Fewer infants achieved the principal outcome of survival without moderate to severe neurodevelopmental disability at 24 months in the faster feeds arm (802/1224 vs 848/1246). The stochastic cost-effectiveness analysis showed a likelihood of worse outcomes for faster feeds compared with slower feeds. CONCLUSIONS: The stochastic cost-effectiveness analysis shows faster feeds are broadly equivalent on cost grounds. However, in terms of outcomes at 24 months age (corrected for prematurity), faster feeds are harmful. Faster feeds should not be recommended on either cost or effectiveness grounds to achieve the primary outcome.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".