Salivary cortisol during memory encoding in pregnancy predicts postpartum depressive symptoms: a longitudinal study
Bibliographic record
Abstract
INTRODUCTION: Postpartum depression (PPD) is a common disorder that substantially decreases quality of life for both mother and child. In this longitudinal study, we investigated whether emotional memory, salivary cortisol (sCORT) or alpha-amylase during pregnancy predict postpartum depressive symptoms. METHODS: Forty-four pregnant women (14 euthymic women with a diagnosis of major depressive disorder [MDD] and 30 healthy women) between the ages of 19 and 37 years (mean age = 29.5±4.1 years) were longitudinally assessed in the 2nd trimester of pregnancy (12-22 weeks of gestational age) and again at 14-17 weeks postpartum. Depressive symptoms were assessed using the Edinburgh Postnatal Depression Scale (EPDS). RESULTS: Follow-ups were completed for 41 women (7% attrition). Postpartum EPDS scores were predicted by sCORT collected immediately after an incidental encoding memory task during pregnancy (b=-0.78, t -2.14, p=0.04). Postpartum EPDS scores were not predicted by positive (p=0.27) or negative (p=0.85) emotional memory. CONCLUSIONS: The results of this study indicate that higher levels of sCORT during a memory encoding task in the 2nd trimester of pregnancy are associated with lower postpartum EPDS scores. While the hypothalamus-pituitary-adrenal (HPA) axis has long been associated with the neurobiology of MDD, the role of the HPA axis in perinatal depression deserves more attention.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".