Maternal prenatal state anxiety symptoms and birth weight: A pilot study
Bibliographic record
Abstract
Abstract Many women suffer from new or worsening anxiety during pregnancy. In this pilot study, we investigated the effect of timing and severity of prenatal state anxiety symptoms on reduced birth weight. We hypothesized that: (1) Women with state anxiety symptoms during mid-gestation would deliver newborns with lower birth weight in comparison to participants with symptoms in early gestation and (2) compared to women with lower anxiety symptoms (< 50th percentile), women with medium-to-high state anxiety symptoms (> 50th percentile) would have lower birth weight offspring. The sample consisted of the first 30 pregnant women who agreed to participate in this pilot study. We assessed anxiety symptoms, using the State-Trait Anxiety Inventory during early and mid-gestation. We obtained birth weight from clinical charts. A hierarchical multiple regression showed that, after controlling for covariates, state anxiety symptoms in mid-gestation were associated with lower infant birth weight [F(9, 7) = 20.30, p<.001]. However, birth weight did not differ as a function of the severity of maternal state anxiety [F(1, 23)=.14, p=.71 and F(1, 24)=1.76, p=.20., respectively]. Clearly, our pilot data need replication. Once statistical significance is established with larger samples, it will be informative to examine the clinical significance of those findings.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".