Special issues in the management of depression in women.
Bibliographic record
Abstract
Depression is more prevalent in women than in men, which may be related to biological, hormonal, and psychosocial factors. Four depressive conditions are specific to women: premenstrual dysphoric disorder (PMDD), depression in pregnancy, postpartum depression, and depression related to perimenopause or menopause. Antidepressant therapy with selective serotonin reuptake inhibitors and venlafaxine has demonstrated efficacy in PMDD. Both continuous and intermittent dosing regimens were effective at usual but not at low dosages. Despite reluctance of some women to take medication for depression during pregnancy and breastfeeding, substantial evidence suggests that antidepressants are safe and efficacious during these periods, while untreated depression has negative consequences for both mother and child. In peri- or postmenopausal women with depression, estrogen may enhance the effects of antidepressant medications, although a pooled analysis of data in women aged 50 years or over treated with venlafaxine found that remission rates were similar in those who were taking estrogen and those who were not. The management of women with depression can be done safely and effectively using antidepressants and alternative interventions throughout the life cycle.
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 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.053 | 0.017 |
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".