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Record W4380482107 · doi:10.9778/cmajo.20220039

Prescribing patterns and factors associated with sodium–glucose cotransporter-2 inhibitor prescribing in patients with diabetes mellitus and atherosclerotic cardiovascular disease

2023· article· en· W4380482107 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCMAJ Open · 2023
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsWomen's College HospitalToronto Public HealthHealth Sciences CentreUniversity Health NetworkSunnybrook Health Science Centre
FundersNovavaxJanssen PharmaceuticalsSanofiGlaxoSmithKline
KeywordsDiabetes mellitusMedicineAtherosclerotic cardiovascular diseaseDiseaseType 2 Diabetes MellitusInternal medicineCotransporterEndocrinologySodiumChemistry

Abstract

fetched live from OpenAlex

<h3>Background</h3> Sodium–glucose cotransporter-2 (SGLT2) inhibitors are cardioprotective agents in patients with type 2 diabetes mellitus and atherosclerotic cardiovascular disease (CVD). Since little is known about their uptake in atherosclerotic CVD, we examined SGLT2 inhibitor prescribing trends and identified potential disparities in prescribing patterns. <h3>Methods</h3> We conducted an observational study using linked population-based health data in Ontario, Canada, from April 2016 to March 2020 of patients aged 65 years or older with concomitant type 2 diabetes and atherosclerotic CVD. To examine prevalent prescribing of SGLT2 inhibitors (canagliflozin, dapagliflozin and empagliflozin), we constructed 4 cross-sectional yearly cohorts from Apr. 1 to Mar. 31 (2016/17, 2017/18, 2018/19 and 2019/20). We estimated prevalent SGLT2 inhibitor prescribing by year and by subgroups, and identified factors associated with SGTL2 inhibitor prescribing using multivariable logistic regression. <h3>Results</h3> There were 208 303 patients in our overall cohort (median age 74.0 yr [interquartile range 68.0–80.0 yr], 132 196 [63.5%] male). Although SGLT2 inhibitor prescribing increased over time, from 7.0% to 20.1%, statin prescribing was initially 10-fold higher and later threefold higher than SGLT2 inhibitor prescribing. In 2019/20, SGLT2 inhibitor prescribing was roughly 50% lower among those aged 75 years or older than among those younger than 75 years (12.9% v. 28.3%, <i>p</i> &lt; 0.001) and in women than in men (15.3% v. 22.9%, <i>p</i> &lt; 0.001). Age 75 years or older, female sex, history of heart failure and kidney disease, and low income were independent factors of lower SGLT2 inhibitor prescribing. Among physician specialists, visits to endocrinologists and family physicians were stronger factors of SGLT2 inhibitor prescribing than cardiologist visits. <h3>Interpretation</h3> We found that 1 in 5 patients with diabetes and atherosclerotic CVD were prescribed SGLT2 inhibitors in 2019/20, whereas statins were prescribed for 4 of every 5 patients. Although SGLT2 inhibitor prescribing increased over the study period, disparities in adoption by age, sex, socioeconomic status, comorbidities and physician specialty remained.

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.

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 score0.007
Threshold uncertainty score0.759

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 head0.026
GPT teacher head0.221
Teacher spread0.195 · 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