Brazilian Psychiatric Association consensus for the management of psychiatric emergencies in pregnancy and postpartum period
Bibliographic record
Abstract
Introduction: Emergencies in the pregnancy and postpartum are less frequent than in other groups, but not uncommon. However, the literature on the subject is scarce and controversial. Objective: The objective of this article is to present some recommendations for the management of the most common psychiatric emergencies that may occur in pregnancy or postpartum period. Method: These procedures were focused on the discussion and integration of the findings from peer-reviewed published research on the topic. We searched electronic database PubMed. Relevant abstracts were identified using the following search terms: Psychotropics medications at pregnancy and breastfeeding: (((psychotropic medications) AND (pregnancy)) OR (psychotropic medications)) AND (breastfeeding) - Psychiatric emergencies: (((((((psychiatric emergencies) AND (pregnancy)) OR (psychiatric emergencies)) AND (postpartum)) OR (psychiatric emergencies)) AND (peripartum)) OR (psychiatric emergencies)) AND (breastfeeding). Inclusion criteria included papers published (or in press) about pregnancy or breastfeeding or postpartum from December 2000 to January 2021 that focused on agitation in psychiatric emergencies. Main Results: We present recommendation for pharmacological treatment, psychomotor agitation, suicide behavior, psychotic disorders and mania, severe depression, and substance use disorders. Conclusion: Although many of the recommendations are empirical, it is already possible to rely on information that provides better results and safety for the patient and her infant.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".