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Record W3013076265 · doi:10.4103/cjrm.cjrm_51_19

Screening for gestational diabetes in pregnancy in Northwestern Ontario

2020· article· en· W3013076265 on OpenAlexaffvenueabout
Jenna Poirier, Ribal Kattini, Len Kelly, Sharen Madden, Brenda Voth, Joe Dooley, Brent Marazan, Ruben Hummelen

Bibliographic record

VenueCanadian Journal of Rural Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsNOSM University
Fundersnot available
KeywordsGestational diabetesPregnancyObstetricsMedicineDiabetes mellitusGestationEndocrinologyBiology

Abstract

fetched live from OpenAlex

INTRODUCTION: We estimate the screening and prevalence of gestational diabetes mellitus (GDM) in a primarily first nations obstetrical population in Northwestern Ontario. METHODS: The study is an 8-year retrospective analysis of all gestational glucose challenge and tolerance tests performed at the Sioux Lookout Meno Ya Win Health Centre (SLMHC) laboratory from 1 January, 2010 to 31 December, 2017. Test, gestational timing and completion rate of screening were recorded, and GDM prevalence was calculated on the tested population. Screening completion rates were recorded for the subset of women who delivered at SLMHC from 2014 to 2017. RESULTS: The average annual GDM prevalence was 12%, double the Ontario rate. Over the 8-year period, 513 patients were diagnosed with GDM among the 4298 patients screened. Patients were screened with the 2-step (90%) or the 1-step (10%) protocol. Screening occurred <20 weeks in 3%; 54% occurred in <28 weeks and 40% >28 weeks. Seventy percent of the tests were from remote nursing stations. The screening completion rate for women delivering at SLMHC in 2017 was 80.8%. CONCLUSION: The prevalence of GDM in Northwestern Ontario is twice the provincial rate. Most screening used the 2-step protocol; early screening was underused. Improvements in screening programming are underway and future research may match surveillance rates and results to GDM outcomes.

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)0.000
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 score0.577
Threshold uncertainty score0.966

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 head0.044
GPT teacher head0.283
Teacher spread0.239 · 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

Citations7
Published2020
Admission routes3
Has abstractyes

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