[Association between depression during pregnancy and low birth weight in neonates: a Meta analysis].
Bibliographic record
Abstract
OBJECTIVE: To study the association between depression during pregnancy and low birth weight in neonates, and to provide a scientific basis for the prevention of low birth weight. METHODS: Cohort studies on the association between depression during pregnancy and low birth weight were collected and a Meta analysis was performed. Data were extracted independently by two investigators, and quality assessment was performed according to Newcastle-Ottawa Scale. The Egger's test was used to evaluate publication bias. RESULTS: A total of 12 cohort studies with 37 192 samples were included. The results of the Meta analysis showed that depression during pregnancy was associated with low birth weight (Z=2.08, P=0.038), and the neonates whose mothers had depression during pregnancy tended to have a high risk of low birth weight (RR=1.303, 95%CI: 1.015-1.672). The sensitivity analysis showed that the results of this Meta analysis were stable and reliable, and the Egger's test showed no publication bias. CONCLUSIONS: Depression during pregnancy may be a risk factor for low birth weight in neonates.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.034 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".