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Record W2945078351 · doi:10.22374/cjgim.v14i2.282

Oral Hypoglycemics in Patients with Type 2 Diabetes and Peripheral Artery Disease

2019· article· en· W2945078351 on OpenAlexaffvenue
Luke Rannelli, Eric Kaplovitch, Sonia S. Anand

Bibliographic record

VenueCanadian Journal of General Internal Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsHamilton Health SciencesMcMaster UniversityPopulation Health Research InstituteUniversity of TorontoUniversity of Calgary
Fundersnot available
KeywordsMedicineDiabetes mellitusGlycemicAmputationAdverse effectType 2 diabetesInternal medicineCritical limb ischemiaIschemiaRevascularizationPeripheralDiseaseRandomized controlled trialSurgeryArterial diseaseVascular diseaseCardiologyEndocrinologyMyocardial infarction

Abstract

fetched live from OpenAlex

Worldwide, in 2010, 202 million people were living with peripheral artery disease (PAD), with a prevalence between 3–12%. The prevalence of PAD is 3 times greater in diabetic patients compared to those with normal glycemia. PAD of the limbs is associated with increased cardiovascular morbidity and mortality, as well as major adverse limb events including acute limb ischemia and amputation. These risks are particularly high in patients who smoke and/or have type 2 diabetes. The goal of treatment in diabetic patients with PAD is to prevent cardiovascular events and prevent further peripheral artery stenosis leading to limb ischemia, and amputation. Poor glycemic control contributes to atherosclerotic progression; however, no randomized control trial evidence exists that demonstrates improved glycemic control reduces the risk of PAD. Oral diabetic medications are designed to lower glucose levels, reduce symptoms and the microvascular complications of diabetes without the inconvenience of daily injections. However, the data supporting the benefit of these medications in diabetic populations with concurrent PAD are limited. We review the evidence for oral hypoglycemic agents in the treatment of patients with concurrent PAD and 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 score0.007
Threshold uncertainty score0.986

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)0.0010.000

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 head0.007
GPT teacher head0.215
Teacher spread0.208 · 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

Citations1
Published2019
Admission routes2
Has abstractyes

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