Human Milk Calorie Guide: A Novel Color-Based Tool to Estimate the Calorie Content of Human Milk for Preterm Infants
Bibliographic record
Abstract
Fixed-dose fortification of human milk (HM) is insufficient to meet the nutrient requirements of preterm infants. Commercial human milk analyzers (HMA) to individually fortify HM are unavailable in most centers. We describe the development and validation of a bedside color-based tool called the ‘human milk calorie guide’(HMCG) for differentiating low-calorie HM using commercial HMA as the gold standard. Mothers of preterm babies (birth weight ≤ 1500 g or gestation ≤ 34 weeks) were enrolled. The final color tool had nine color shades arranged as three rows of three shades each (rows A, B, and C). We hypothesized that calorie values for HM samples would increase with increasing ‘yellowness’ predictably from row A to C. One hundred thirty-one mother’s own milk (MOM) and 136 donor human milk (DHM) samples (total n = 267) were color matched and analyzed for macronutrients. The HMCG tool performed best in DHM samples for predicting lower calories (<55 kcal/dL) (AUC 0.87 for category A DHM) with modest accuracy for >70 kcal/dL (AUC 0.77 for category C DHM). For MOM, its diagnostic performance was poor. The tool showed good inter-rater reliability (Krippendorff’s alpha = 0.80). The HMCG was reliable in predicting lower calorie ranges for DHM and has the potential for improving donor HM fortification practices.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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; 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".