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Record W4400481855 · doi:10.1093/humrep/deae108.097

O-091 Severe maternal and neonatal morbidity among gestational carriers: a population-based cohort study

2024· article· en· W4400481855 on OpenAlexaffabout
Mihaela Ivanova, Joel G. Ray, Jonas Shellenberger, Jessica Pudwell, Maria P. Vélez

Bibliographic record

VenueHuman Reproduction · 2024
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity of TorontoSt. Michael's HospitalQueen's University
Fundersnot available
KeywordsMedicineObstetricsCohortCohort studyPopulationGestational ageGestationPregnancyPediatricsEnvironmental healthInternal medicineBiology

Abstract

fetched live from OpenAlex

Abstract Study question Are gestational carriers at higher risk of severe maternal (SMM) or neonatal (SNM) morbidity, compared to patients using unassisted conception or in vitro fertilization (IVF)? Summary answer A higher risk of SMM and adverse pregnancy outcomes is seen among gestational carriers, without evident neonatal morbidity. What is known already The use of gestational carriers (GC) also known as “surrogate pregnancy” is increasing. Gestational carriage may be associated with adverse outcomes linked to both IVF techniques and exposure to foreign fetal antigens. Adverse outcomes of GC pregnancies are largely unknown. Study design, size, duration Retrospective population-based cohort study of 937,938 births, derived from existing administrative health datasets. Data were linked using unique for birthing woman and child identifiers and analyzed at ICES Ontario. The study excluded participants with untreated infertility, those utilizing ovulation induction or intrauterine insemination, and those with multifetal gestation. Participants/materials, setting, methods Singleton births at > 20 weeks gestation in Ontario, 2012-2021. Pregnancy characteristics were obtained from the Ontario Birth Registry database. Exposure groups were i) unassisted conception (comparison group 1), ii) IVF (comparison group 2), or iii) GC. Primary outcomes included validated composite outcomes of i) SMM and ii) SNM. Secondary outcomes included hypertensive disorders of pregnancy, cesarean delivery, preterm birth, and postpartum hemorrhage. Modified Poisson regression models generated relative risks adjusted for birthing woman’s characteristics (aRR). Main results and the role of chance Of all singleton births, 937,938 (97.7%) were from unassisted conception, 20,958 (2.2%) from IVF, and 956 (0.1%) from GC. GC were more likely to be parous, reside in a lower-income area, and have higher rates of chronic hypertension. GC were more likely than unassisted conception women to be older and be non-smokers, with some opposite trends when compared to IVF recipients. Respective rates of SMM were 2.4%, 4.6% and 7.1%. The aRR were 3.07 (95% CI 2.44-3.87) comparing GC to unassisted conception, and 1.88 (95% CI 1.46-2.42) comparing GC to IVF. Respective rates of SNM were 6.0%, 9.1% and 6.5%, generating aRR of 1.21 (95% CI 0.95-1.54) for GC vs. unassisted conception, and 0.78 (95% CI 0.60-1.00) for GC vs. IVF. Respective rates of hypertensive disorders were 6.6%, 11.6% and 13.9%. The aRR were 1.89 (1.61 - 2.21) comparing GC to unassisted conception, 1.42 (1.19 - 1.69) comparing GC to IVF. Respective rates of postpartum hemorrhage were 5.7%, 10.5% and 13.9%, generating aRR of 2.87 (2.43 - 3.38) for GC vs. unassisted conception, and 1.40 (1.17 - 1.67) for GC vs. IVF. Limitations, reasons for caution The databases did not include certain baseline details, including the reasons for opting for a GC and the sources of gametes. There is a chance that the measured association may be distorted or confounded by the unmeasured variables. Wider implications of the findings GC appear to be at an elevated risk of maternal morbidity, without evident risk of neonatal morbidity. Further studies are needed to understand the mechanisms involved. Eligibility for GC should be determined using consistent selection criteria, and consideration should be given to specialized obstetrical care to promote optimal pregnancy outcomes. Trial registration number Not applicable

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.022
GPT teacher head0.312
Teacher spread0.290 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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