Subclinical Mastitis May Not Reduce Breastmilk Intake During Established Lactation
Bibliographic record
Abstract
OBJECTIVE: This study determined the effect of subclinical mastitis (SCM) on infant breastmilk intake. DESIGN: Participants (60 Ghanaian lactating mothers and their infants) were from periurban communities in the Manya Krobo district of Ghana in 2006-2007. Bilateral breastmilk samples were obtained once between months 3 and 6 postpartum and tested for SCM using the California mastitis test (CMT) and the sodium/potassium (Na/K) ratio. Infants' 12-hour breastmilk intake was assessed by test weighing. CMT scoring for SCM diagnosis was scaled as >or=1 = positive (n = 37) and <1 = negative (n = 23). SCM diagnosis was confirmed as a Na/K ratio of >1.0 (n = 14). RESULTS: Breastmilk intake was nonsignificantly lower among infants whose mothers had elevated Na/K ratios of >1.0 (-65.1 g; 95% confidence interval -141.3 g, 11.1 g). Infants whose mothers were positive for SCM with both CMT and Na/K ratio criteria had significantly lower breastmilk intake (-88.9 g; 95% confidence interval -171.1 g, -6.9 g) compared to those whose mothers tested either negative with both tests or positive on only one. Infant weight (p < 0.01) and frequency of feeding (p = 0.01) were independently associated with breastmilk intake. However, the effect of SCM on breastmilk intake disappeared when infant weight and feeding frequency were included in a multiple linear regression model. CONCLUSIONS: The results of this study did not show an effect of SCM on breastmilk intake among 3-6-month-old infants. A larger sample size with a longitudinal design will be needed in future studies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".