Gestational hypertensive disease and birthweight discordance in twin pregnancies: a systematic review and meta-analysis
Bibliographic record
Abstract
The objective of this systematic review was to explore the association between gestational hypertensive disease (GHD) and birthweight discordance in twin pregnancies. PubMed, Embase, Web of Science and Cochrane Library were systematically searched for studies reporting the risk of birthweight discordance in twin pregnancies complicated compared with those not complicated by GHD from establishment until July 2021. Risk of bias was assessed with the Newcastle-Ottawa Scale. According to the classification of GHD, sub-group analyses reporting cases with gestational hypertension (GH), chronic hypertension (CH) and preeclampsia (PE) were performed separately. Stratification by twin chorionicity (dichorionic (DC) and monochorionic (MC)) was also conducted. When there was substantial heterogeneity (I2 ≥ 50%), the random effect mode was used to estimate the pooled risk ratio, otherwise the fixed effect model was used. Nine studies (303,204 twin pregnancies) were included. GHD (OR 1.45, 95% CI 1.41–1.49) was a risk factor for intertwin birthweight discordance [PE (OR 1.69, 95% CI 1.33–2.16); CH (OR 1.59, 95% CI 1.46–1.73); GH (OR1.45, 95%Cl 1.10–1.92]. After stratification, birthweight discordance was related to GHD (OR 2.51, 95% CI 2.01–3.14), GH (OR 2.08, 95% CI 1.33–3.25) and PE (OR 2.74, 95% CI 2.09–3.61) in DC pregnancies, but no longer associated with GHD and PE in MC group. Twin gestations complicated with GHD, especially in DC pregnancies, were at significantly higher risk of birthweight discordance.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".