Between a rock-a-bye and a hard place: mood disorders during the peripartum period
Bibliographic record
Abstract
Mood disorders including major depressive disorder and bipolar disorder are common during and after pregnancy. Timely identification and appropriate management of mood episodes is essential to maximize maternal well-being and minimize adverse outcomes. Failure to do so results in maternal suffering and impaired child bonding, and has the potential for devastating outcomes including suicide and infanticide. Women are routinely screened for unipolar depression during or after pregnancy but not for bipolar disorder, in spite of the fact that childbirth is associated with a major risk for onset or exacerbation of bipolar disorder. Delays in detection as well as misdiagnosis of bipolar disorder as major depressive disorder may put women at risk of many adverse consequences, including symptom exacerbation, psychiatric hospitalization, and suicide. A thorough psychiatric assessment is necessary to establish diagnosis, to address safety issues, and to formulate a treatment plan. Treatment of mood disorders during pregnancy is complicated by the potential risks of fetal exposure to psychotropic medications, and the use of these medications during the postpartum period may result in infant medication exposure through breastmilk. These risks of psychotropic medication exposure must be weighed against the risk of untreated mood disorders. This review will discuss the pathophysiology, epidemiology, diagnosis, and treatment of mood disorders during pregnancy and the postpartum period. Screening tools that can be used in the primary care and obstetrics settings to assist in identifying women with peripartum mood disorders will also be discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".