Associations between maternal hormonal biomarkers and maternal mental and physical health of very low birth weight infants
Bibliographic record
Abstract
The purpose of this study was to determine whether maternal mental and physical health is associated with maternal testosterone and cortisol levels, parenting of very low birth weight infants, physical exercise, and White vs non-White race. A total of 40 mothers of very low birth weight infants were recruited from a neonatal intensive care unit at a University Hospital in the Southeast United States. Data were collected through a review of medical records, standardized questionnaires, and biochemical measurement. Maternal mental and physical health status using questionnaires as well as maternal testosterone and cortisol levels using an enzyme immunoassay were measured four times (birth, 40 weeks postmenstrual age [PMA], and 6 and 12 months [age of infant, corrected age]). General linear models showed that higher testosterone levels were associated with greater depressive symptoms, stress, and poorer physical health at 40 weeks PMA, and at 6 and 12 months. High cortisol levels were associated with greater anxiety at 40 weeks PMA; however, with better mental and physical health at 40 weeks PMA, and 6 and 12 months. Physical activity was associated with lower maternal perceived stress at 12 months. Maternal health did not differ by race, except anxiety, which was higher in White than non-White mothers after birth. As very low birth weight infants grew up, maternal physical health improved but mental health deteriorated. Testosterone and cortisol levels were found to be positively correlated in women but testosterone was more predictive of maternal mental and physical health than cortisol. Indeed testosterone consistently showed its associations with maternal health. Maternal stress might be improved through regular physical exercise.
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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 | 0.002 |
| 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".