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Record W4399555753 · doi:10.1016/j.jogc.2024.102582

Endometriosis, Severe Maternal Morbidity, and the Effect of Infertility: Population-Based Cohort Study

2024· article· en· W4399555753 on OpenAlexafffundvenueabout
Maria P. Vélez, O. L. Chapman, Olga Bougie, Jessica Pudwell, Wenbin Li, Susan B. Brogly

Bibliographic record

VenueJournal of Obstetrics and Gynaecology Canada · 2024
Typearticle
Languageen
FieldMedicine
TopicEndometriosis Research and Treatment
Canadian institutionsQueen's University
FundersCanadian Institutes of Health ResearchOntario Ministry of Health and Long-Term Care
KeywordsEndometriosisMedicineInfertilityObstetricsFertilityPoisson regressionGynecologyPopulationRelative riskFemale infertilityCohort studyCohortPregnancyConfidence intervalInternal medicine

Abstract

fetched live from OpenAlex

This population-based cohort evaluated the association between endometriosis and severe maternal morbidity (SMM), and the mediating effect of infertility and fertility treatment. Included were all singleton deliveries in Ontario between 2006 and 2014. Modified Poisson regression generated adjusted relative risks. Mediation analysis estimated the direct effect of endometriosis and indirect effect through infertility and mode of conception. 787 449 deliveries were included (19 099, 2.4% with endometriosis). SMM occurred in 29.0 per 1000 deliveries among women with endometriosis, in contrast to 18.2 per 1000 deliveries among those without endometriosis-corresponding to an adjusted relative risk of SMM of 1.43 (95% CI 1.31-1.56). Mediation analysis demonstrated that the effect of endometriosis on SMM was independent of infertility or fertility treatment. We conclude that SMM in women with endometriosis appears to be due to the disease itself and not to infertility or related treatments.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.655
Threshold uncertainty score0.695

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.274
Teacher spread0.265 · 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 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

Citations2
Published2024
Admission routes4
Has abstractyes

Explore more

Same venueJournal of Obstetrics and Gynaecology CanadaSame topicEndometriosis Research and TreatmentFrench-language works237,207