Paroxetine Levels in Postpartum Depressed Women, Breast Milk, and Infant Serum
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to determine the concentrations of paroxetine in maternal serum, breast milk, and infant serum samples and to estimate infant exposure through breastfeeding. METHOD: A total of 25 sample sets was obtained: I sample set each from 23 mother-infant dyads and 2 sample sets from 1 mother-infant dyad. All mothers met DSM-IV criteria for major depressive disorder. The maternal fixed dosage of paroxetine was 10, 20, or 40 mg/day for a minimum of 30 days before the samples were drawn. Samples were collected 6 hours after dose intake, and the concentration of paroxetine in each sample was determined using gas chromatography/mass spectrometry. The analytic method employed in this study is the most sensitive to date, with the ability to detect drug concentrations as low as 0.1 ng/mL. RESULTS: Detectable levels of paroxetine were present in all maternal serum samples and in 24 of the 25 breast milk samples. In all of the infant serum samples, the paroxetine concentrations were below the lower limit of quantification. No unusual adverse effects were reported in any of the infants. CONCLUSION: The results of this study demonstrate that paroxetine, like the other selective serotonin reuptake inhibitors studied to date, is excreted into the breast milk of nursing mothers. The mean infant dose of paroxetine was 1. 1% of the maternal dose. Although no short-term adverse effects were reported in any of the infants in this study, future studies are needed to address a more systematic method for observing and recording any adverse effects. In addition, future studies should incorporate follow-up studies in order to evaluate possible long-term effects of paroxetine exposure.
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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 | 0.001 | 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".