Use of psychotropic medications during pregnancy: perception of teratogenic risk among physicians in two Latin American countries
Bibliographic record
Abstract
OBJECTIVE: Pregnant women and their health care professionals commonly believe that use of medications during pregnancy may be harmful to the unborn fetus. The objective of this study was to evaluate the risk perception of psychotropic drug use in pregnancy among physicians in different medical specialties. METHOD: This was a convenience survey conducted at outpatient clinics in the cities of Recife, Brazil, and La Plata, Bahía Blanca, and Buenos Aires, Argentina. Physicians who agreed to participate were asked to rate their perception of teratogenic risk among different classes of drugs, which included antidepressants, antipsychotics, anticonvulsants, and benzodiazepines. RESULTS: Two hundred and thirty-eight physicians completed the survey (response rate, 98%). These included psychiatrists, obstetricians, neurologists, cardiologists, gastroenterologists, and general practitioners. Among different specialties, a minority of psychiatrists perceived psychotropic drugs to be highly teratogenic (antidepressants, 12.5%; antipsychotics, 15%; benzodiazepines, 25%) as compared with other specialties (p < 0.003 for each drug class). There was no difference in perceived risk of antiepileptic drugs among specialties, including psychiatrists. CONCLUSION: The risk associated with use of psychotropic drugs in pregnancy was overestimated by physicians of all medical specialties, except psychiatry. All physicians should be aware of the safety/risk of psychotropic agents in pregnancy, as they may be required to give advice and/or prescribe these drugs to pregnant women.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".