Quetiapine Augmentation in Lactation
Bibliographic record
Abstract
Treating psychiatric disorders with pharmacotherapy in the breast-feeding period presents a dilemma as such treatment carries the risk of infant exposure to medication through breast milk. However, failure to institute pharmacotherapy in postnatal women in need of such treatment exposes both mother and baby to detrimental effects of the illness. Because women presenting with psychiatric disorders during the postpartum period often have complex sets of symptoms, monotherapy may not be sufficient for symptom resolution. In this case series of 6, we examined levels of psychotropic medications secreted in breast milk and performed developmental assessments of the exposed babies with the Bayley Scales of Infant Development, Second Edition. In 3 of the 6 cases, no medication was detected in the breast milk; in all but 1 case, estimated levels of infant medication exposure were calculated to be less than 0.01 mg/kg per day for each medication. Four of the 6 babies scored as being within normal limits on the Bayley Scales of Infant Development, Second Edition, whereas 2 showed mild developmental delays. In comparison to the 4 cases of typical development, the 2 showing mild delays did not have higher estimated levels of psychotropic medication exposure through breast milk. Based on these results, in our limited sample, there appears to be low levels of infant exposure to the medications through breast milk; no association was seen between developmental outcomes and exposure through breast milk of multiple pharmacological agents. These results should be interpreted with caution, and vigilance should be exercised when advising women on combinations of medications for severe mental illness who choose to nurse.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".