Applying suggested new terminology and definitions for human milk feeding in the Alberta Pregnancy Outcomes and Nutrition (APrON) longitudinal pregnancy cohort
Bibliographic record
Abstract
The complexity of human milk-feeding behaviours may not be captured using simpler definitions of “exclusive” and “non-exclusive” breastfeeding. New definitions have been suggested to describe variation in these behaviours more fully but have not been widely applied. We applied the new definitions to data derived from 3-day human milk-feeding diaries. Participants ( n = 1091) recorded the number, beginning/end time, and modes of feeding of infants aged 3 months. Data were used to create six exclusive groups according to feeding mode(s): (1) human milk at-breast only; (2) human milk at-breast and human milk in a bottle; (3) human milk at-breast and infant formula in a bottle; (4) human milk at-breast and human milk and infant formula mixed in the same bottle; (5) human milk at-breast, human milk in a bottle, and infant formula in a bottle (not mixed); and (6) a bottle that sometimes contained human milk and sometimes infant formula (not mixed), never at-breast. Differences in maternal and infant characteristics were examined among groups. Fifty-seven percent fed at-breast only (Group 1). Those in Group 1 spent a similar amount of time feeding directly at-breast (median 132 (IQR 98–172) min/day) as those in Groups 2 (124 (95–158)), 3 (143 (100–190)), and 5 (114 (84–142)) ( p > 0.05), indicating that adding bottle feeding did not always reduce the time infants were fed at-breast. Applying new suggested definitions to describe human milk-feeding behaviours from the mothers’ perspective highlights the complexity of patterns used and warrants further application and research to explore impacts on health outcomes.
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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 | 0.073 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".