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Record W2995994882 · doi:10.2337/dc19-1487

Risk of Psychiatric Disorders and Suicide Attempts in Emerging Adults With Diabetes

2019· article· en· W2995994882 on OpenAlexafffundabout
Marie‐Eve Robinson, Marc Simard, Isabelle Larocque, Jai Shah, Meranda Nakhla, Elham Rahme

Bibliographic record

VenueDiabetes Care · 2019
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsMcGill University Health CentreMcGill UniversityDouglas Mental Health University InstituteChildren's Hospital of Eastern OntarioInstitut National de Santé Publique du QuébecUniversity of Ottawa
FundersFonds de Recherche du Québec - SantéInstitut de recherche, Centre universitaire de santé McGill
KeywordsMedicineDiabetes mellitusPsychiatryMood disordersRetrospective cohort studyHazard ratioCohortCohort studyMoodPoison controlMedical emergencyInternal medicineConfidence interval

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the longitudinal risks of psychiatric disorders in adolescents and emerging adults with versus without diabetes. RESEARCH DESIGN AND METHODS: We conducted a retrospective cohort study in Quebec, Canada, using linked health administrative databases of adolescents (age 15 years) with and without diabetes and without prior psychiatric disorders between 1997 and 2015, followed to age 25 years. RESULTS: Our cohort included 3,544 individuals with diabetes and 1,388,397 without diabetes. Individuals with diabetes were more likely to suffer from a mood disorder (diagnosed in the emergency department or hospital) (adjusted hazard ratio 1.33 [95% CI 1.19-1.50]), attempt suicide (3.25 [1.79-5.88]), visit a psychiatrist (1.82 [1.67-1.98]), and experience any type of psychiatric disorder (1.29 [1.21-1.37]) compared with their peers without diabetes. CONCLUSIONS: Between the ages of 15 and 25 years, the risks of psychiatric disorders and suicide attempts were substantially higher in adolescents and emerging adults with versus without 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)0.000
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 score0.011
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.313
Teacher spread0.303 · 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

Citations46
Published2019
Admission routes3
Has abstractyes

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