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Record W3110550487 · doi:10.1093/humrep/deaa270

Effect of publicly funded assisted reproductive technology on maternal and infant outcomes: a pre- and post-comparison study

2020· article· en· W3110550487 on OpenAlexaffabout
Shu Qin Wei, Marianne Bilodeau‐Bertrand, Ernest Lo, Nathalie Auger

Bibliographic record

VenueHuman Reproduction · 2020
Typearticle
Languageen
FieldMedicine
TopicAssisted Reproductive Technology and Twin Pregnancy
Canadian institutionsMcGill UniversityUniversité de MontréalInstitut National de Santé Publique du QuébecCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsAssisted reproductive technologyMedicinePregnancyConfidence intervalPreeclampsiaLive birthRelative riskObstetricsPublic healthInfant mortalityDemographyPopulationEnvironmental healthInfertilityNursing

Abstract

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STUDY QUESTION: Does publicly funded assisted reproductive technology result in improved maternal and infant outcomes? SUMMARY ANSWER: Publicly funded ART in Quebec was associated with reduced risks of preeclampsia, cesarean delivery, preterm birth, low birth weight and other adverse outcomes. WHAT IS KNOWN ALREADY: Publicly funded ART programs that provide free access to single embryo transfer are known to decrease the rate of multiple pregnancy, but the impact on other pregnancy outcomes is unknown. STUDY DESIGN, SIZE, DURATION: We conducted a pre- and post-comparison study of 597 416 pregnancies conceived between July 2008 and September 2015 in Quebec, Canada, a region where public funding of ART began in August 2010. PARTICIPANTS/MATERIALS, SETTING, METHODS: We included all pregnant women who conceived by ART (n = 14 309) or spontaneously (n = 583 107) and delivered a live or stillborn infant in hospitals of Quebec. The main exposure measure was conception before versus during the publicly funded ART program. Outcomes included measures of maternal and infant morbidity and mortality. We estimated risk ratios (RR) and 95% confidence intervals for the association of publicly funded ART with maternal and infant outcomes using log-binomial regression models adjusted for maternal characteristics. MAIN RESULTS AND THE ROLE OF CHANCE: In this study, 2638 pregnancies were conceived by ART before, and 11 671 were conceived by ART, during public funding. Compared with no public funding, ART funding was associated with reduced risks of severe maternal morbidity (RR 0.64, 95% CI 0.50-0.83), preeclampsia (RR 0.55, 95% CI 0.44-0.68), cesarean delivery (RR 0.83, 95% CI 0.77-0.89), preterm birth (RR 0.67, 95% CI 0.60-0.75), low birth weight (RR 0.63, 95% CI 0.55-0.72), severe neonatal morbidity (RR 0.75, 95% CI 0.57-0.99) and neonatal intensive care unit admission (RR 0.65, 95% CI 0.53-0.78). When multiple pregnancies were excluded, ART funding continued to be associated with a lower risk of preeclampsia (RR 0.61, 95% CI 0.48-0.79) and preterm birth (RR 0.85, 95% CI 0.73-0.99). However, ART funding was associated with increased risk of gestational diabetes. LIMITATIONS, REASONS FOR CAUTION: We had no information on the type of ART, number of in-vitro fertilization cycles or number of embryos transferred. We lacked data on body mass index, ethnicity and smoking and cannot rule out residual confounding. WIDER IMPLICATION OF THE FINDINGS: Our findings suggest that publicly funded ART programs that encourage single embryo transfer may have substantial benefits for a range of maternal and infant outcomes, beyond prevention of multiple births. STUDY FUNDING/COMPETING INTEREST(S): This study was supported by grant 6D02363004 from the Public Health Agency of Canada. N.A. acknowledges a career award from the Fonds de recherche du Québec-Santé (34695). The authors declare no competing interests. TRIAL REGISTRATION NUMBER: N/A.

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 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)0.001
metaresearch head score (Gemma)0.002
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 score0.091
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.331
Teacher spread0.306 · 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

Citations10
Published2020
Admission routes2
Has abstractyes

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