MétaCan
Menu
Back to cohort
Record W2767445572 · doi:10.1111/dme.13543

History of mood or anxiety disorders and risk of gestational diabetes mellitus in a population‐based cohort

2017· article· en· W2767445572 on OpenAlexafffundabout
Qendresa Beka, Samantha L. Bowker, Anamaria Savu, Dawn Kingston, Jeffrey Johnson, Padma Kaul

Bibliographic record

VenueDiabetic Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsGestational diabetesMedicineAnxietyOdds ratioMood disordersMoodPopulationPregnancyDiabetes mellitusObstetricsCohortAnxiety disorderPsychiatryPediatricsGestationInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

AIM: To examine the association between mood and anxiety disorders and the development of gestational diabetes mellitus in a retrospective population-based cohort study. METHODS: Clinical data from a provincial perinatal health registry were linked to physician claims, hospitalization records and emergency visits to identify any diagnoses of mood or anxiety disorders in the 2 years prior to pregnancy and a subsequent diagnosis of gestational diabetes during pregnancy. The study population included all singleton pregnancies in the Canadian province of Alberta from 1 April 2000 to 31 March 2010. Generalized estimating equations were used to determine the adjusted odds ratio of gestational diabetes, comparing women with and without a history of mood or anxiety disorders. RESULTS: Among 373 674 pregnancies from 253 911 women, 25.7% had a history of mood or anxiety disorders, and 3.8% developed gestational diabetes. The multivariate-adjusted odds of developing gestational diabetes were higher among women with a history of mood or anxiety disorders (odds ratio 1.10, 95% CI 1.06-1.14). CONCLUSIONS: Women with a history of mood or anxiety disorders had a moderately increased risk of developing gestational diabetes.

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 score0.012
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.017
GPT teacher head0.283
Teacher spread0.266 · 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

Citations20
Published2017
Admission routes3
Has abstractyes

Explore more

Same venueDiabetic MedicineSame topicGestational Diabetes Research and ManagementFrench-language works237,207