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Record W2990844888 · doi:10.1176/appi.ps.201900009

Factors Associated With Diabetes Care Quality Among Patients With Schizophrenia in Ontario, Canada

2019· article· en· W2990844888 on OpenAlexafffundabout
Jonathan H. Hsu, Andrew Calzavara, Simone N. Vigod, Thérèse A. Stukel, Tara Kiran, Paul Kurdyak

Bibliographic record

VenuePsychiatric Services · 2019
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsWomen's College Hospital
FundersCanadian Institutes of Health ResearchHospital for Sick ChildrenUniversity of TorontoWomen's College HospitalOntario Ministry of Health and Long-Term CareMedical Psychiatry AllianceGilead SciencesInstitute for Clinical Evaluative SciencesCentre for Addiction and Mental Health
KeywordsMedicineOdds ratioReceiptOddsSchizophrenia (object-oriented programming)Logistic regressionDiabetes mellitusFamily medicineMental healthAmbulatory careConfidence intervalRetrospective cohort studyPsychiatryHealth careInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The authors investigated demographic, clinical, and service-utilization factors that affected the quality of diabetes care among patients with schizophrenia. METHODS: This was a retrospective cohort study of adults with schizophrenia and diabetes (N=26,259) in Ontario, Canada. Quality of care was based on receipt of three guideline-concordant diabetes care procedures between 2011 and 2013. A cumulative logit regression model was used to determine characteristics associated with optimal testing. RESULTS: Factors associated with optimal diabetes testing included more frequent outpatient psychiatrist visits (odds ratio [OR]=1.28, 95% confidence interval [CI]=1.20-1.37) and primary care visits for nonmental health reasons (OR=2.10, 95% CI=1.85-2.39). High-frequency primary care visits for mental health reasons, any hospitalizations, and emergency visits for mental health reasons were associated with lower odds of testing. CONCLUSIONS: Diabetes quality of care may be contingent on receipt of medically focused primary care, psychiatric stability, and receipt of specialist psychiatric care.

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 score0.076
Threshold uncertainty score0.737

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 integrity0.0000.000
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 head0.011
GPT teacher head0.232
Teacher spread0.221 · 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

Citations6
Published2019
Admission routes3
Has abstractyes

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