The Impact of Diet Quality on Gestational Weight Gain Across Trimesters
Bibliographic record
Abstract
mothers who delivered preterm.Samples from countries with a Human Development Index < 0.8 and studies with a sample size of one were excluded from analysis.Random effects models were used to estimate mean estimates across studies by HM type (e.g., colostrum, transition, and mature).Colostrum HM was considered as HM collected 4d, transition between 5 -14d, and mature as 15d.Estimates were aggregated within studies that reported multiple time points.Heterogeneity was assessed using Cochrane-I 2 .Results: Of the 48 studies that reported energy density, 30 that provided sufficient data were included in this meta-analysis, which represented 1,713 HM samples.50% of the studies were conducted in the USA or Canada, 36% in the European Union, and 14% in other countries.Milk extraction methods included electric pump expression (36%), a mix of pump and hand expression (26.6%) and other manual/pump methods unspecified.Thirty-three percent evaluated energy density using bomb calorimetry, 30% using mid-infrared spectroscopy, 27% calculated energy density from macronutrient composition, and 10% did not specify.Mean energy density in preterm HM was 68.05 kcal/100mL(95% CI: 65.52, 70.59)[Q¼384.71,df¼29, p< 0.001; I 2 ¼89.8%].Energy density in colostrum, transition, and mature milk was 66.64 kcal/100mL(95% CI: 63.06, 70.21), 71.93 kcal/100mL (95% CI: 67.95, 75.90), and 70.54 kcal/ 100mL (95% CI: 66.56, 74.51), respectively.Estimates of heterogeneity varied from 78.1% to 94.4%.Conclusions: These data provide the most current energy density estimates for preterm HM which can better help practitioners determine appropriate preterm infant feedings.
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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 | 0.026 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.030 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".