Deuterium Oxide Dilution Method to Quantify Human Milk Intake Volume of Infants: Systematic Review – A Contribution from the ConcePTION Project
Bibliographic record
Abstract
Background: Global health organizations recommend breastfeeding, but maternal pharmacotherapy can disrupt this due to safety concerns. Physiologically-based pharmacokinetic (PBPK) models predict medication transfer through breastfeeding, relying on validated milk intake volume data. However, literature mainly focused on different measurement methods, or such intake data were collected without systematic review. This systematic review therefore aims to gather data on human milk intake volume derived using the (dose-to-the-mother) deuterium oxide dilution method, allowing comparison with literature. Additionally, it aims to explore effects of maternal conditions on milk intake volume. Methods: PubMed, Embase, Web of science, Cochrane library, Scopus and CINAHL were searched for studies on the dilution method and breastfeeding in healthy infants. Risk of Bias was assessed using the Newcastle-Ottawa Scale (NOS) and the Risk of Bias 2 (RoB2) tool. Data on mean human milk intake volume were extracted and synthesized (mL/day and mL/kg/day) throughout infancy. Results: Sixty studies (34 countries) reported on milk intake volume of 5502 infants. This intake was best described by logarithmic regression y(mL/kg/day) = 149.4002 -0.2268*x -0.1365*log(x) (x=postnatal age, days). Maternal conditions showed no significant influence on human milk intake, except for maternal smoking (reduction). Conclusion: This function corresponds with previous literature, particularly between 1.5 to 12 months. Limited availability of early infancy data underscores the need for additional data in future PBPK modelling to enhance informed healthcare decisions and improved outcomes for mother and infant.
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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 | 0.019 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".