Combination of body mass index and body fat percentage in middle and late pregnancy to predict pregnancy outcomes in patients with gestational diabetes in Wenzhou, China: a single-centre retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: The present study aimed to evaluate whether body mass index (BMI) and body fat percentage (BFP) could be used to predict pregnancy outcomes in patients with gestational diabetes mellitus (GDM). DESIGN: Retrospective cohort study. SETTING: Wenzhou Medical University Affiliated Second Hospital (Zhejiang Province, China). Clinical data were collected from electronic medical records. PARTICIPANTS: Data from 683 patients with GDM admitted to the Wenzhou Medical University Affiliated Second Hospital between January 2019 and December 2021 were retrospectively analysed. OUTCOME MEASURES: Pregnancy outcomes. RESULTS: The results showed that pregnant women with BFP ≥33% were more prone to abnormal amniotic fluid volume, abnormal blood pressure and anaemia (p<0.05). Additionally, these patients were more likely to experience postpartum haemorrhage and macrosomia, as well as risk factors associated with caesarean section at labour (p<0.05). BMI exhibited a strong predictive value for abnormal blood pressure (OR 1.170; 95% CI 1.090 to 1.275), anaemia (OR 1.073; 95% CI 1.016 to 1.134), caesarean section (OR 1.150; 95% CI 1.096 to 1.208) and macrosomia (OR 1.169; 95% CI 1.063 to 1.285). Additionally, classified BFP had a predictive value for abnormal amniotic fluid volume (OR 3.196; 95% CI 1.294 to 7.894), abnormal blood pressure (OR 2.321; 95% CI 1.186 to 4.545), anaemia (OR 1.817; 95% CI 1.216 to 2.714), and caesarean section (OR 1.734; 95% CI 1.270 to 2.367). CONCLUSIONS: The results suggest that patients with GDM with BFP ≥33% were more likely to experience unfavourable pregnancy outcomes, undergo caesarean section and develop macrosomia. The combination of BMI with classified BFP could better predict abnormal blood pressure and caesarean section in patients with GDM during the middle and late stages of pregnancy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".