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Record W3037140441 · doi:10.4103/cjrm.cjrm_71_19

Demographics, prevalence and outcomes of diabetes in pregnancy in NW Ontario

2020· article· en· W3037140441 on OpenAlexaffvenueabout
Len Kelly, Ruben Hummelen, Ribal Kattini, Jenna Poirier, Sharen Madden, Holly Ockenden, Joseph Dooley

Bibliographic record

VenueCanadian Journal of Rural Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsNOSM University
Fundersnot available
KeywordsMedicinePregnancyGestational diabetesObstetricsParity (physics)Diabetes mellitusDiabetes in pregnancyDemographicsRelative riskConfidence intervalGynecologyRetrospective cohort studyFetal macrosomiaGestationDemographyEndocrinologyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Diabetes in pregnancy confers increased risk. This study examines the prevalence and birth outcomes of diabetes in pregnancy at the Sioux Lookout Meno Ya Win Health Centre (SLMHC) and other small Ontario hospitals. METHODS: This was a retrospective study of maternal profile: age, parity, comorbidities, mode of delivery, neonatal birth weight, APGARS and complications. Data were compared to other Ontario hospitals offering an equivalent level of obstetrical services. RESULTS: Type 2 diabetes mellitus in pregnancy is far more prevalent in mothers who deliver at SLMHC (relative risk [RR]: 20.9, 95% confidence interval [CI]: 16.0-27.2); the rates of gestational diabetes (GDM) are double (RR: 2.0, 95% CI: 1.7-2.3). SLMHC mothers with diabetes were on average 5 years younger and of greater parity with increased substance use. Neonates largely had equivalent outcomes except for increased macrosomia, neonatal hypoglycaemia and hyperbilirubinaemia in GDM pregnancies. CONCLUSION: Patients with diabetes in pregnancy at SLMHC differ substantially from mothers delivering at Ontario hospitals with a comparable level of service. Programming and resources must meet the service needs of these 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)0.000
metaresearch head score (Gemma)0.001
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 score0.201
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.021
GPT teacher head0.263
Teacher spread0.242 · 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

Citations3
Published2020
Admission routes3
Has abstractyes

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