Managing Women With Obesity in Pregnancy: Scope of Practice in the Wake of the Obesity Epidemic [23N]
Bibliographic record
Abstract
INTRODUCTION: Obesity is a complex chronic disease affecting increasing numbers of reproductive aged women. Despite ongoing research efforts, many knowledge gaps remain when caring for women with obesity in pregnancy. Currently, there is no clearly defined, comprehensive standard of care for pregnant women with obesity. Consequently, obstetrical care providers have developed different approaches. In this study, we explored these approaches and how management of women with obesity differs from that of normal weight patients. METHODS: Qualitative concept maps were generated through individual in-depth mapping sessions with obstetricians (n=7) in Edmonton, Alberta, Canada. Concept maps were analyzed thematically and major themes and sub-themes used to inform survey development. Institutional ethics approval was obtained. RESULTS: Overall, seven dominant themes emerged from our thematic analysis: 1) Obstetricians define obesity differently; 2) Communication with patients with obesity should be “direct” and “honest”; 3) Understanding fetal wellbeing is more challenging in patients with obesity in both the pre-natal and intra-partum period; 4) Obstetricians recommend induction of labor for large for gestational age fetuses but not maternal obesity; 5) Women with obesity have abnormal labor; 6) Obstetricians expect more complications, alter their surgical approach, and adjust their threshold for Cesarean deliveries in patients with obesity; and 7) Education and knowledge translation about obesity in pregnancy is inadequate. CONCLUSION: Concept mapping provided insights into how present-day obstetrical care is affected by obesity. This information has led to the development of a quantitative survey for obstetricians that will more broadly assess practice patterns for the management of women with obesity in 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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".