Optimizing Gestational Weight Gain in Twin Pregnancies
Bibliographic record
Abstract
INTRODUCTION: Data on the optimal gestational weight gain (GWG) in twin pregnancies and the implications of inadequate GWG on pregnancy complications in this population are limited.OBJECTIVE: To evaluate the implications of inadequate GWG in twin pregnancies, identify the optimal range of GWG, and develop and implement a new care pathway aimed at optimizing GWG in this population. METHODS: We conducted the following separate (but strongly related) projects to address the objectives described above: (1) A systematic review and meta-analysis; (2) A retrospective cohort study; (3) A national survey among Canadian maternal-fetal-medicine specialists; (4) A new care pathway aimed at optimizing GWG in twins. RESULTS: (1) The meta-analysis revealed that over half (56.8%) of women with twins experience GWG outside of recommendations. Low-GWG was associated with preterm birth, while high-GWG was associated with preeclampsia. (2) Our retrospective study confirmed the findings of the meta-analysis in our local population. In addition, we identified new, outcome-based optimal GWG range in twins, which had better correlation with outcomes compared with current guidelines. (3) Through the survey we identified considerable inconsistencies and potential barriers for optimal GWG in twins; (4) We designed and implemented a new care pathway to optimize GWG in our Twins Clinic at Sunnybrook. CONCLUSIONS: GWG is an important and modifiable risk factor for preterm birth and other pregnancy complications in twin gestations, making its optimization an important goal of antenatal care in this population.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".