Weight-depression association in a high-risk maternal population
Bibliographic record
Abstract
OBJECTIVE: Both maternal depression and overweight carry potential adverse effects on perinatal health and are inter-related. We explored the relationship between weight and depressive symptoms in a high-risk maternal population. METHODS: We administered the Edinburgh Postnatal Depression Scale (EPDS) to all women attending the Motherisk Clinic at The Hospital for Sick Children between October 2007 and April 2010. We explored possible associations between the EPDS scores, maternal weight and other characteristics. RESULTS: The study population consisted of 352 women, 43.7% of whom were pregnant, with a variety of exposures. Twenty seven percent of the study population had diagnosed depression. Depressed women had a significantly higher body weight compared to non-depressed women (p = 0.016). The same finding remained significant in the pregnant sub-group. The EPDS score, for the entire study population, was significantly correlated with body weight (p = 0.027). Use of antidepressants was an independent predictor of maternal weight in a multivariate regression analysis. CONCLUSIONS: There is a strong association between maternal weight and depressive symptoms, whether diagnosed or not. Antidepressant therapy is an independent predictor of maternal weight. Since both depression and maternal overweight may adversely affect pregnancy outcome, and are treatable, addressing both is essential for optimal pregnancy management.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".