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Record W4392343662 · doi:10.1016/j.rcsop.2024.100429

Rurality is associated with lower likelihood of dipeptidyl peptidase 4 inhibitor use for treatment intensification

2024· article· en· W4392343662 on OpenAlexafffund
Danielle K. Nagy, Lauren Bresee, Dean T. Eurich, Scot H. Simpson

Bibliographic record

VenueExploratory Research in Clinical and Social Pharmacy · 2024
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersUniversity of Alberta
KeywordsRuralityMedicineResidenceMetropolitan areaMetforminLogistic regressionConfoundingDipeptidyl peptidase-4 inhibitorDemographyRetrospective cohort studyEnvironmental healthType 2 diabetesRural areaDiabetes mellitusInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Background: Antihyperglycemic drug utilization studies are conducted frequently and describe the uptake of new drug therapies across may jurisdictions. An increasingly important, yet often absent, aspect of these studies is the impact of rurality on drug utilization. Objectives: The objective of this study was to explore the association between place of residence (rural, urban, metropolitan) and the use of dipeptidyl peptidase 4 inhibitors (DPP-4i) for first treatment intensification of type 2 diabetes. Methods: A retrospective cohort study was conducted from April 1, 2008 to March 31, 2019 of new metformin users. A multivariable logistic regression analysis was performed to determine the association between place of residence (using postal codes) and likelihood of DPP-4i dispensing. Results: After adjusting for confounders, analysis revealed that rural-dwellers are less likely to have a DPP-4i dispensed, compared with metropolitan-dwellers (aOR:0.64; 95%CI:0.61-0.67) and over-time, the uptake in rural areas was slower. Conclusions: This study demonstrates that rurality can have an impact on drug therapy decisions at first treatment intensification, with respect to the utilization of new therapies.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.391
GPT teacher head0.507
Teacher spread0.116 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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