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Record W3021867965 · doi:10.1210/jendso/bvaa046.2249

MON-LB8 A Preconception Lifestyle Intervention Maintained Throughout Pregnancy Improves Some Gestational and Neonatal Outcomes in Women With Obesity and Infertility

2020· article· en· W3021867965 on OpenAlexaff
Mehdi Rouissi, Marie-Andrée Lévesque, Marie-Christine Hébert, Farrah Jean-Denis, Matea Bélan, Marie‐France Langlois, Youssef Ainmelk, Belina Carranza‐Mamane, Marie-Hélène Pesant, Jean‐Patrice Baillargeon

Bibliographic record

VenueJournal of the Endocrine Society · 2020
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsMedicinePregnancyObstetricsOverweightBody mass indexInfertilityObesityGestational ageLive birthGynecologyRandomized controlled trialBirth weightInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background : Obesity in women of childbearing age is associated with infertility and increases significantly the risks of many pregnancy and neonatal complications. Adopting a healthy lifestyle prior conception and maintaining it during pregnancy may reduce these complications. Our aim was therefore to determine whether a lifestyle program targeting women with obesity and infertility and maintained during pregnancy improves gestational and neonatal outcomes. Methods : We report on 46 women who became pregnant and had available outcome data during pregnancy and at birth, among 127 women with infertility and obesity (body mass index, BMI ≥30 kg/m²), or overweight with PCOS (BMI ≥27 kg/m²), who were enrolled in a lifestyle randomized-controlled trial. Participants were randomized to the control group (CG, n=20), who received standard of care, or the lifestyle group (LSG, n=26), who followed a lifestyle program alone for 6 months, and then in combination with usual fertility care for 18 months or until the end of pregnancy. Pregnancy and neonatal outcomes were retrospectively retrieved from mothers’ and newborns’ medical records. Results : At enrollment, both groups were similar for age (29.3 vs 31.0 years), BMI (38.7 vs 38.4 kg/m2) and waist circumference (113.7 vs 112.7 cm). Preconception weight loss was significantly higher in the LSG compared to the CG (4.86 kg vs 1.21 kg, p=0.013), but gestational weight gains were similar (+10.83 vs +10.52 kg, p=0.987). During pregnancy, groups did not differ for the rates of preeclampsia, gestational diabetes or other clinical outcomes, but significantly less women in the LSG required insulin for treatment of their gestational diabetes (12.5% vs 42.1%, p=0.027) as well as urgent cesarean section due to failure of vaginal delivery (0.0% vs 21.1%, p=0.021). Regarding neonatal outcomes, there was no significant difference between groups for gestational age, weight at birth and head circumference, as well as rates of prematurity, LGA, SGA, birth defects or other clinical outcomes, but babies from the LSG displayed significantly lower tricipital skinfolds (4.73 mm vs 5.72 mm, p=0.031) and trends for lower sum of four skinfolds (16.61 mm vs 19.06 mm, p=0.056) and increased length at birth (50.82 cm vs 49.63 cm, p=0,053). Conclusion : In women with obesity and infertility, our lifestyle program initiated prior to fertility treatments and maintained throughout pregnancy improved their preconception weight and lifestyle, but not their gestational weight gain. Such intervention was nonetheless effective to reduce significantly some clinically relevant pregnancy and neonatal complications. If these results are replicated in a larger sample, it would strongly suggest that women with obesity should be supported to adopt a healthy lifestyle prior conception in order to increase their likelihood of giving birth to a healthy baby.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.011
GPT teacher head0.284
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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