The association between isolated oligohydramnios at term and risk of small for gestational age: A meta-analysis
Bibliographic record
Abstract
Background Due to the scarcity of available data on the association between isolated oligohydramnios and the risk of small for gestational age (SGA), we undertook a meta-analysis to investigate this relationship. Methods PubMed (Medline), Web of Science, Scopus, and Science Direct were systematically queried up to February 26, 2024. Analysis was conducted using the random-effects model. Heterogeneity was evaluated among studies utilizing the chi-square test (χ 2 ) and the I 2 statistic. Additionally, we conducted regression tests including Egger’s and Begg’s tests to assess publication bias. We employed the modified Newcastle–Ottawa Scale (NOS) to assess the quality of observational articles. Statistical significance was set at a p -value less than 0.05 using Stata software, version 13. Results In the present meta-analysis, seven studies met the inclusion criteria and were included in the present systematic review and meta-analysis. The association between isolated oligohydramnios at term and the risk of SGA in crude studies was 2.22 (95% CI: 1.49, 2.94; I 2 = 82.5%). Conversely, in adjusted studies, the association was 2.18 (95% CI: 1.78, 2.57; I 2 = 0.0%). Conclusion The present meta-analysis indicates that isolated oligohydramnios is a significant risk factor for the SGA. Therefore, monitoring to diagnose SGA should be done in mothers with isolated oligohydramnios.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.061 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".