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Record W4286744398 · doi:10.1177/17539447221113946

Polypharmacy among older individuals with heart failure: trends between 2000 and 2017 in the province of Quebec, Canada

2022· article· en· W4286744398 on OpenAlexafffundabout
Alexandre Campeau Calfat, Marc Simard, Amina Ouali, Claudia Blais, Caroline Sirois

Bibliographic record

VenueTherapeutic Advances in Cardiovascular Disease · 2022
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsInstitut National de Santé Publique du QuébecUniversité Laval
FundersFonds de Recherche du Québec - SantéUniversité Laval
KeywordsPolypharmacyMedicineHeart failureGerontologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Pharmacological management of heart failure and comorbidities may result in polypharmacy, but there are few population-based studies that portray the use of medications over time. We aimed to describe the trends in polypharmacy and medication use in older adults with heart failure. METHODS: We performed a study including all adults >65 years with heart failure between 2000 and 2017 using health administrative databases in Quebec, Canada. Medication use was ascertained by the presence of at least one claim in each year. We defined three levels of polypharmacy: ⩾10, ⩾15 and ⩾20 different medications/year, and evaluated the use of guideline-recommended and potentially inappropriate medications. We calculated age- and sex-standardized proportions of users each year. RESULTS: The use of ⩾10, ⩾15 and ⩾20 medications increased from 62.2%, 30.6% and 12.2% in 2000 to 71.9%, 43.9% and 22.7%, respectively, in 2017. The combination of β-blocker and angiotensin-converting enzyme inhibitor (ACEI)/angiotensin II receptor blocker (ARB) was used by 30.4% of individuals in 2000 and 45.5% in 2017. ACEI/ARB users decreased from 65.8% in 2000 to 62.1% in 2017. Potentially inappropriate medication use decreased over time. CONCLUSION: Polypharmacy is significant among older adults with heart failure. Implications of such medication burden should be investigated.

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)0.001
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 score0.322
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.260
Teacher spread0.250 · 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

Citations5
Published2022
Admission routes3
Has abstractyes

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