Is Maternal Obesity a Predictor of Shoulder Dystocia?
Bibliographic record
Abstract
OBJECTIVE: To explore the relationship between maternal obesity and shoulder dystocia while controlling for the potential confounding effects of other variables associated with obesity. METHODS: We performed a case-control study of provincial delivery records audited by the Northern and Central Alberta Perinatal Outreach Program. Risk factors evaluated were selected based on previously published studies. Cases and controls were drawn from 45,877 live singleton cephalic vaginal deliveries weighing more than 2500 g between January 1995 and December 1997. There were 413 cases of shoulder dystocia (0.9% incidence). Controls (n= 845) were randomly chosen from the remainder of the target population to create a 1:2 case/control ratio. Univariate analysis with calculation of odds ratios (ORs) was used to determine which of the chosen risk factors were significantly related to the incidence of shoulder dystocia. Multivariable regression analyses were then used to determine the independently associated variables, and the adjusted ORs were obtained for each relevant risk factor. RESULTS: Maternal obesity was not significant as an independent risk factor for shoulder dystocia after adjusting for confounding variables (adjusted OR 0.9; 95% confidence interval [CI] 0.5, 1.6). Fetal macrosomia was the single most powerful predictor. The adjusted ORs were 39.5 (95% CI 19.1, 81.4) for birth weight greater than 4500 g and 9.0 (95% CI 6.5, 12.6) for birth weight between 4000 and 4499 g. CONCLUSION: The strongest predictors of shoulder dystocia are related to fetal macrosomia. For obese nondiabetic women carrying fetuses whose weights are estimated to be within normal limits, there is no increased risk of shoulder dystocia. (Obstet Gynecol 2003;101:24-7. © 2003 by The American College of Obstetricians and Gynecologists.)
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".