The role of gestational weight gain disorders in symptoms of maternal postpartum depression
Bibliographic record
Abstract
OBJECTIVE: To examine the association of gestational weight gain (GWG), categorized according to 2009 IOM guidelines as adequate, inadequate, and excessive, with symptoms of mental disorders perceived by mothers after childbearing as anhedonia, anxiety, and depression, defined by the Edinburgh Postnatal Depression Scale (EPDS). Previous studies indicated that disorders related to GWG are associated with an increased risk of postpartum psychological distress. METHODS: A prospective cohort study took place at the Policlinico Abano Terme, Italy, from May 2016 to November 2018. RESULTS: The sample included 1268 healthy at term puerperae, 557 (43.9%) with adequate, 388 (30.6%) with inadequate, and 323 (25.5%) with excessive GWG. Mean EPDS scores were comparable among inadequate, adequate, and excessive GWG groups. However, mean factor scores for anhedonia and anxiety were significantly higher (P = 0.041 and P = 0.001, ANOVA) in mothers with excessive GWG. Conversely, factor scores for depression were significantly higher (P = 0.008, ANOVA) in mothers with inadequate GWG. CONCLUSION: It was found that excessive GWG across an uncomplicated pregnancy is a warning sign of symptoms of anhedonia and anxiety, whereas inadequate GWG is a significant indicator of symptoms of depression. These relationships highlight the potential for interventions directed toward psychosocial support to have beneficial effects upon GWG.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".