Human milk feeding for moderate and late preterm infants at age 2 months: Insights from a cluster randomized controlled trial 2-month follow-up
Bibliographic record
Abstract
Objective: Human milk (HM) is the optimal nutrition for infants; preterm infants demonstrate shorter HM feeding duration. Care interventions may increase HM feeding among preterm infants after NICU discharge. We compared Alberta Family Integrated Care (FICare) versus Standard Care on HM feeding in preterm infants at age 2 months. Methods: We conducted a follow-up of a cluster randomized controlled trial of 455 infants and their mothers with data linked to the infant's 2-month public health visit. We used partial proportional odds to model group differences and factors associated with feeding type: exclusive HM (EHM), Non-EHM, or no HM (NHM). Results: Compared to Standard Care, mothers in Alberta FICare were less likely to provide EHM versus NHM. There was no group difference between EHM and Non-EHM. Mothers with higher education who were on maternity leave or employed were more likely to provide EHM. Infants who received EHM at discharge were more likely to continue at age 2 months. Higher maternal breastfeeding self-efficacy at discharge was associated with a greater likelihood of EHM. Conclusion: Alberta FICare was not associated with EHM feeding at age 2 months. Innovation: Different factors predicted the three HM feeding categories, suggesting the need to individualize feeding supports.Trial Registration.ClinicalTrials.gov Identifier NCT02879799, retrospectively registered August 26, 2016.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".