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Record W1776071502 · doi:10.12927/hcq.2015.24250

Drug Use among Seniors on Public Drug Programs in Canada, 2012

2015· article· en· W1776071502 on OpenAlexaffabout
Jeff Proulx, Jordan Hunt

Bibliographic record

VenueHealthcare Quarterly · 2015
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsCanadian Institute for Health Information
Fundersnot available
KeywordsDrugDrug classMedicineMedical prescriptionFamily medicinePublic healthDrug Utilization ReviewPrescription drugLong-term careGerontologyMedical emergencyPharmacologyNursing

Abstract

fetched live from OpenAlex

Seniors take more drugs than younger Canadians because, on average, they have a higher number of chronic conditions. Although taking multiple medications may be necessary to manage these conditions, it is important to consider the benefits and risks of each medication and the therapeutic goals of the patient. This article provides an in-depth look at the number and types of drugs used by seniors using drug claims data from the CIHI's National Prescription Drug Utilization Information System Database, representing approximately 70% of seniors in Canada. In 2012, almost two-thirds (65.9%) of seniors on public drug programs had claims for five or more drug classes, while 27.2% had claims for 10 or more, and 8.6% had claims for 15 or more. The most commonly used drug class was statins, used by nearly half (46.6%) of seniors. Nearly two-thirds (60.9%) of seniors living in long-term care (LTC) facilities had claims for 10 or more drug classes. Proton pump inhibitors were the most commonly used drug class among seniors living in LTC facilities (used by 37.0% of seniors in LTC facilities), while statins ranked seventh (29.8%).

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 score0.217
Threshold uncertainty score0.694

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.184
GPT teacher head0.377
Teacher spread0.193 · 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

Citations48
Published2015
Admission routes2
Has abstractyes

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