Relationship of Subclinical Mastitis in Ghanaian Women and Breast Milk Intake by Infants
Bibliographic record
Abstract
Human subclinical mastitis (SCM) is inflammation of mammary tissue without any overt manifestations but is associated with lactation failure, sub-optimal infant growth during the early postpartum period, and increased risk of mother-to-child-transmission of HIV via breast milk. Subclinical mastitis (SCM) has been associated with infant growth faltering but the mechanism explaining this association remains unknown. We hypothesized that SCM is associated with reduced breast milk intake resulting in diminished growth. Ghanaian mothers who were 3-6 months postpartum were screened for SCM using the California mastitis test (CMT). A CMT score of ≥ 1 was categorized as SCM positive (N=37); a CMT score < 1 was considered SCM negative (N=23). SCM diagnosis was confirmed by an elevated breast milk sodium-potassium ratio (Na/K > 1.0). We measured infants’ 12-hour breast milk intake in both groups of mothers using the test weighing methodology. Breast milk intake tended to be lower among infants whose mothers had elevated Na/K > 1.0 (-65.1 g; 95% CI: -141.3 g, 11.1 g). Infants whose mothers were positive for SCM with both CMT and Na/K criteria had significantly lower breast milk intake (-88.9 g; 95% CI: -171.1 g, -6.9 g) compared to those whose mothers tested either negative with both tests or positive on only one. However, in the multiple linear regression analysis, infant weight (p<0.01) and frequency of feeding (p<0.01) but not maternal SCM status were associated with breast milk intake (p = .12). When infant weight and feeding frequency were considered, the observed direct effect of SCM on infant breast milk intake was no longer significant. However, lower breast milk intake (p = .12 in MLRA) coupled with limited subjects and only 12 hr breast intake data warrant further investigation and concerns.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".