Bibliographic record
Abstract
#### What you need to know Depression in pregnancy affects up to 10% of women, with higher rates in low and middle income countries, a rate only slightly lower than in the postpartum period.1 2 Yet, as few as 20% of pregnant women with depression receive adequate treatment.3 4 This is problematic because depression can profoundly affect a woman’s sense of wellbeing, relationships, and quality of life. Untreated or incompletely treated depression can also have adverse consequences for the offspring. Systematic reviews show an increase in markers of infant morbidity such as preterm birth, childhood emotional difficulties, behaviour problems, and, in some studies, poor cognitive development.5 6 Antenatal depression also is one of the strongest risk factors for postnatal depression, a condition linked to developmental problems in children.6 7 Severe depression can result in suicide, a major cause of maternal death.8 9 Perinatal suicides have been associated with lack of active treatment.10 Barriers to treatment include stigma, lack …
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".