Antipsychotic Medication during Pregnancy and Lactation in Women with Schizophrenia: Evaluating the Risk
Bibliographic record
Abstract
OBJECTIVE: To review studies investigating the following: whether exposing developing infants to antipsychotic medication during pregnancy and lactation is associated with increased risks of teratogenic, neonatal, and long-term neurobehavioural sequelae; whether schizophrenia itself affects pregnancy outcome; and whether the course of schizophrenia symptoms is altered by pregnancy and lactation. METHOD: We summarize the results from articles identified via a MedLine search for the period January 1, 1966, to December 1, 2001. RESULTS: Women with schizophrenia are at increased risk for poor obstetrical outcomes, including preterm delivery, low birth weight, and neonates who are small for their gestational age. A lack of information in the literature makes it difficult to comment on the relative risk of exposing developing infants to atypical antipsychotics. However, typical antipsychotics appear to carry an increased risk of congenital malformations when the fetus is exposed to phenothiazines during weeks 4 to 10 of gestation. Lack of information also precludes an understanding of whether changes associated with pregnancy and lactation significantly alter the course of schizophrenia symptoms. CONCLUSION: Research is needed so that physicians may more accurately inform women about the relative risks of using antipsychotic medications during pregnancy and lactation. Increased knowledge about the risks of medication exposure will allow clinicians to limit treatment to situations in which the risk of untreated maternal illness outweighs the risk of exposing a developing infant to medications.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".