Parity and low birth weight and preterm birth: a systematic review and meta‐analyses
Bibliographic record
Abstract
OBJECTIVE: To systematically review the risks of pregnancy outcomes among women of different parity. MATERIAL AND METHODS: Electronic databases were searched for studies, in English language, in which primary objective was to assess association between parity and pregnancy outcomes. Meta-analyses were performed and unadjusted odds ratios (ORs) and mean differences along with 95% confidence interval (CI) were calculated. MAIN OUTCOME MEASURES: Low birth weight (LBW), preterm birth (PTB), small for gestational age (SGA), birth weight, and gestational age. RESULTS: Forty-one studies, most with moderate risk of bias were included. Nulliparity was associated with increased unadjusted odds of LBW (OR 1.41, 95% CI 1.26, 1.58) and SGA (OR 1.89, 95% CI 1.82, 1.96) and reduction in birth weight (weighted mean difference -282 g, 95% CI -486, -79 g) but not PTB (OR 1.13, 95% CI 0.96, 1.34). Grand multiparity and great grand multiparity were not associated with LBW (OR 1.10, 95% CI 0.95, 1.32 and OR 0.92, 95% CI 0.78, 1.09) or PTB (OR 0.96, 95% CI 0.77, 1.19 and OR 1.32, 95% CI 0.61, 2.83). CONCLUSIONS: Nulliparity was associated with a significantly increased unadjusted risk of LBW/SGA birth, whereas grand multiparity and great grand multiparity were not associated with increased risk of pregnancy outcomes.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.023 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".