Bibliographic record
Abstract
Main Program Abstract \n \nWith the recognition of the importance of antenatal mental health for maternal and fetal outcomes there has followed an emphasis on the importance of adequate treatment of mental health in pregnancy. For some women, treatment may include the maintenance or commencement of psychotropic medications. This understanding by clinicians of the importance of maternal mental health may explain the significant rise in rates of use of these agents in pregnancy including rises in antidepressant rates in pregnancy across Australia, USA, Canada and the Netherlands. As rates of use of these agents rise it makes understanding the risks and benefits of these agents for women and offspring important information for all clinicians managing women in pregnancy. \n \nThis talk will give an overview of the indications, uses, risks and benefits of the key classes of psychotropic medications used to treat mental disorders in pregnancy, including antidepressants, mood stabilisers and antipsychotic agents. A discussion of where the use of these medications fit within an overall perinatal mental health management will be outlined as well as other treatment options for mental disorders. Finally, there will be a discussion of key recommendations for antenatal care monitoring for women who are either started or continue to be treated with these agents across pregnancy.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.112 | 0.011 |
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".