A case–control study to examine the association between breastfeeding during late pregnancy and risk of a small‐for‐gestational‐age birth in <scp>L</scp>ima, <scp>P</scp>eru
Bibliographic record
Abstract
Excessive demands on maternal nutritional status may be a risk factor for poor birth outcomes. This study examined the association between breastfeeding during late pregnancy (≥ 28 weeks) and the risk of having a small-for-gestational-age (SGA) newborn, using a matched case-control design (78 SGA cases: birthweight <10th percentile for gestational age; 150 non-SGA controls: 50th percentile <birthweight <90th percentile for gestational age). Between March 2006 and April 2007, project midwives visited daily three government hospitals in Lima, Peru and identified cases and matched controls based on hospital, gestational age, and inter-gestational period. Mothers were interviewed and clinical chart extractions were completed. Factors associated with risk of SGA were assessed by their adjusted odds ratios (aOR) from conditional logistic regression. Exposure to an overlap of breastfeeding during late pregnancy was not associated with an increased risk of having a SGA newborn [aOR=0.58, 95% confidence interval (CI): 0.10-3.30]. However, increased risk was associated with having a previous low-birthweight birth (aOR=6.53; 95% CI: 1.43-29.70) and a low intake of animal source foods (<25th percentile; aOR=2.26; 95% CI: 1.01-5.04), and tended to be associated with being short (<150 cm; aOR=2.05; 95% CI: 0.92-4.54). This study found no evidence to support the hypothesis that breastfeeding during late pregnancy increases the risk for SGA; however, studies with greater statistical power are needed to definitively examine this possible association and clarify whether there are other risks to the new baby, the toddler and the pregnant woman.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".