Influence of the maternal birth status on offspring: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Maternal low birth weight (LBW), preterm birth (PTB) and small-for-gestational age (SGA) birth status have been suggested as precursors for an infant being born LBW, PTB or SGA. OBJECTIVE: To systematically review the risks of infant LBW/PTB/SGA among mothers who were LBW/PTB/SGA. SEARCH STRATEGY: Medline, Embase, CINAHL and bibliographies of identified articles were searched for English language studies. SELECTION CRITERIA: Studies reporting association between maternal birth status and LBW, PTB or SGA outcomes were included. Study quality was assessed for biases in selection, exposure assessment, confounder adjustment, analyses, outcomes assessment and attrition. DATA COLLECTION AND ANALYSIS: Unadjusted data from included studies were extracted by two reviewers. Summary odds ratio (OR) and confidence intervals (CI) were calculated using random effect model. MAIN RESULTS: Twenty-two studies of low to moderate risk of biases were included. Maternal LBW was associated with infant LBW (12 studies, 525,706 participants, OR 2.23, 95% CI 2.11-2.35), PTB (six studies, 331,121 participants, OR 1.57, 95% CI 1.43-1.71) and SGA (three studies, 324,357 participants, OR 1.83, 95% CI 1.43-2.33). Maternal PTB was associated with infant PTB (seven studies, 282,616 participants, OR 1.41, 95% CI 1.26-1.59) and SGA (three studies, 41,590 participants, OR 1.33, 95% CI 1.08-1.64). Maternal SGA was associated with infant PTB (OR 1.58, 95% CI 1.06-2.37) and SGA (OR 2.64, 95% CI 2.28-3.05). CONCLUSIONS: Maternal LBW, PTB or SGA status was associated with their children's LBW/PTB/SGA status. Maternal birth status should be included in risk assessment when planning pregnancy care for individual women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.024 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".