Perinatal Risks of Untreated Depression during Pregnancy
Bibliographic record
Abstract
OBJECTIVE: To review the literature on the perinatal risks involved in untreated depression during pregnancy. METHOD: We searched Medline and medical texts for all studies pertaining to this area up to the end of April 2003. Key phrases entered were depression and pregnancy, depression and pregnancy outcome, and depression and untreated pregnancy. We did not include bipolar depression. RESULTS: While there is wide variability in reported effects, untreated depression during pregnancy appears to carry substantial perinatal risks. These may be direct risks to the fetus and infant or risks secondary to unhealthy maternal behaviours arising from the depression. Recent human data suggest that untreated postpartum depression, not treatment with antidepressants in pregnancy, results in adverse perinatal outcome. CONCLUSION: The biological dysregulation caused by gestational depression has not received appropriate attention: most studies focus on the potential but unproven risks of psychotropic medication. No in-depth discussion of the role of psychotherapy is available. Because they are not aware of the potentially catastrophic outcome of untreated maternal depression, this imbalance may lead women suffering from depression to fear teratogenic effects and refuse treatment.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".