Donor Milk Expression Habits: Can we Favor Hindmilk Banking for Extremely Preterm Infants?
Bibliographic record
Abstract
Background: Extremely preterm infants often receive donor milk. Hindmilk, which is released more than 3 minutes after letdown, could be advantageous due to its elevated levels of fat and calorie density. Donor milk expression habits may influence milk composition but have not yet been investigated. This study aims to assess the practices of milk donors and the feasibility of hindmilk expression. Methods: Active milk donors in Québec were questioned using an online survey about their milk expression habits and whether hindmilk donation would be acceptable to them. Answers were analyzed using mixed methods. Results: Of 181 donors, 126 fully completed the questionnaire (70%); 57% reported expressing donated milk between breastfeeds; 15% reported simultaneously breastfeeding while expressing donated milk from the other breast; 12% reported breastfeeding their baby on each breast, then expressing donated milk (hindmilk). The majority (66%) would be willing to change their habits most or all the time to provide hindmilk for preterm infants. The main themes invoked by respondents in open-ended answers were altruism and gratitude for being able to help others. However, 15% commented on the complexity of milk expression or that adding further complexity might discourage them from donating. Conclusions: Expression practices are variable, which may lead to variability in donor milk composition. Most donors would agree to change their expression habits in favor of giving hindmilk to help the most fragile infants. More information is needed on how changing recommendations for milk expression might impact the supply and composition of donor milk.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".