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Record W2046563875 · doi:10.1001/archinte.161.8.1106

Predictors of Persistence of Use of the Novel Antidiabetic Agent Acarbose

2001· article· en· W2046563875 on OpenAlexaffabout
Vanessa S. Catalan, Julie Couture, Jacques LeLorier

Bibliographic record

VenueArchives of Internal Medicine · 2001
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsHôtel-Dieu de Montréal
Fundersnot available
KeywordsAcarboseMedicineDiscontinuationPersistence (discontinuity)Confidence intervalDemographyPopulationAdverse effectCohortInternal medicineGerontologyDiabetes mellitusEnvironmental healthEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: A carbose is the first of a new class of antidiabetic agents, the alpha-glucosidase inhibitors. This study characterizes and identifies predictors of persistence of use of acarbose. METHODS: Medical, pharmaceutical, and demographic records were extracted for 2 cohorts of patients (social assistance recipients and seniors) from the databases of Quebec's provincial health plan. Patients were eligible for inclusion if they had received their first dispensation of acarbose between August 1, 1996, and December 31, 1997. The observation period included at least 1 year before the first dispensation and a minimum of 4 months after. RESULTS: New users of acarbose included 216 social assistance recipients and 677 seniors who were followed up for 82 914 and 270 041 person-days, respectively. Median persistence with acarbose treatment was 83 days (95% confidence interval, 75-105 days) for social assistance recipients and 105 days (95% confidence interval, 90-119 days) for seniors. In both cohorts, treatment by an endocrinologist vs another physician predicted longer treatment persistence. In the seniors cohort, additional determinants of (earlier) treatment discontinuation included a higher initial daily dose, previous treatment with insulin, and consultation with a gastroenterologist after treatment initiation. CONCLUSIONS: New users of acarbose showed low persistence in 2 cohorts of beneficiaries of Quebec's provincial health plan. Prescribing specialist was an important predictor of persistence in seniors and the socially assisted. The importance of 4 additional factors in seniors only led to hypotheses concerning population differences in treatment expectations and in the occurrence and tolerance of adverse effects.

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)0.001
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 score0.294
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.033
GPT teacher head0.244
Teacher spread0.212 · 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

Citations49
Published2001
Admission routes2
Has abstractyes

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