Predicting Mode of Delivery After an Induction of Labor in Women With an Increased Body Mass Index [33F]
Bibliographic record
Abstract
INTRODUCTION: There is a growing body of literature outlining the adverse consequences of excess body weight in pregnancy. In particular, this has demonstrated an increased rate of cesarean delivery. To date, there is no risk stratification tool to determine which women, with obesity and who are undergoing an induction of labor, are at the greatest risk of cesarean delivery. This study aims to validate the use of a modified Edmonton Obesity Staging System (EOSS) to predict mode of delivery amongst these women. METHODS: A prospective-cohort study was performed at two high obstetrical volume centers in Edmonton, Alberta. A total of 345 nulliparous women, undergoing an induction of labor at term, were recruited. Participating women provided a self-reported health survey and allowed for review of their medical records. The sample population included women with a body mass index (BMI) of ≥25.0 at first antenatal visit. The primary outcome is the rate of cesarean delivery. RESULTS: Overall, 345 women were recruited into this study with a participation rate of 93.7%. This included a sample group of 276 women, with an increased BMI, and a control group of 69 normal-weight women. Preliminary data analysis determined a cesarean delivery rate of 30.4% for the control group and 35.8%, 29.9%, 43.2%, and 90.5% for women assigned an EOSS Stage 0, 1, 2, and 3, respectively. CONCLUSION: A modified version of the EOSS may help stratify the risk of cesarean delivery in nulliparous women, with an increased BMI, who are undergoing an induction of labor.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".