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Record W2150245924 · doi:10.1111/ppe.12174

Do the Causes of Infertility Play a Direct Role in the Aetiology of Preterm Birth?

2015· article· en· W2150245924 on OpenAlexafffundabout
Carmen Messerlian, Robert W. Platt, Barış Ata, Seang Lin Tan, Olga Basso

Bibliographic record

VenuePaediatric and Perinatal Epidemiology · 2015
Typearticle
Languageen
FieldMedicine
TopicAssisted Reproductive Technology and Twin Pregnancy
Canadian institutionsMcGill University Health CentreMcGill University
FundersCanadian Institutes of Health Research
KeywordsMedicineInfertilityObstetricsCohortAssisted reproductive technologyPremature birthCohort studyEtiologyLow birth weightGynecologyPregnancyGestational ageInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: It is well established that singletons born of assisted reproductive technology are at higher risk of preterm birth and other adverse outcomes. What remains unclear is whether the increased risk is attributable to the effects of the treatment alone or whether the underlying causes of infertility also play a role. The aim of this study was to examine whether any of the six categories of causes of infertility were associated with a direct effect on preterm birth using causal mediation analysis. METHODS: We assembled a hospital-based cohort of births delivered at a large tertiary care hospital in Montreal, Canada between 2001 and 2007. Causes of infertility were ascertained through a clinical database and medical chart abstraction. We employed marginal structural models (MSM) to estimate the controlled direct effect of each cause of infertility on preterm birth compared with couples without the cause under examination. RESULTS: The final study cohort comprised 18,598 singleton and twin pregnancies, including 1689 in couples with ascertained infertility. MSM results suggested no significant direct effect for any of the six categories of causes. However, power was limited in smaller subgroup analyses, and a possible direct effect for uterine abnormalities (e.g. fibroids and malformations) could not be ruled out. CONCLUSION: In this cohort, most of the increased risk of preterm birth appeared to be explained by maternal characteristics (such as age, body mass index, and education) and by assisted reproduction. If these findings are corroborated, physicians should consider these risks when counselling patients.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.043
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.047
GPT teacher head0.319
Teacher spread0.272 · 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

Citations6
Published2015
Admission routes3
Has abstractyes

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