Bibliographic record
Abstract
OBJECTIVE: To review existing literature on depression during pregnancy and to provide information for family physicians in order to promote early detection and treatment. QUALITY OF EVIDENCE: MEDLINE was searched from January 1989 through August 2004 using the key words depression, pregnancy, prenatal, and antenatal. Articles focusing on depression during pregnancy were chosen for review; these articles were based on expert opinion (level III evidence) and prospective studies (level II evidence). MAIN MESSAGE: Pregnancy does not safeguard women against depressive illness. The Edinburgh Postnatal Depression Scale is an effective screening tool for identifying women with depressive symptoms during pregnancy. Once diagnosed with major depression, these patients need to be monitored closely for up to a year after delivery. Patients with mild-to-moderate illness should be referred for psychotherapy. More severely ill patients might require additional treatment with antidepressants. The most commonly used antidepressants are selective serotonin reuptake inhibitors and the serotonin and norepinephrine reuptake inhibitor, venlafaxine. For each patient, risk of treatment with an antidepressant needs to be compared with risk of not treating her depressive illness. CONCLUSION: Early detection of depression during pregnancy is critical because depression can adversely affect birth outcomes and neonatal health and, if left untreated, can persist after the birth. Untreated postpartum depression can impair mother-infant attachments and have cognitive, emotional, and behavioural consequences for children.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".