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Record W1996806453 · doi:10.1097/hjh.0b013e32826308b4

A cross-national study of the persistence of antihypertensive medication use in the elderly

2008· article· en· W1996806453 on OpenAlexaboutno aff
Boris LG Van Wijk, William H. Shrank, Olaf H. Klungel, Sebastian Schneeweiß, M. Alan Brookhart, Jerry Avorn

Bibliographic record

VenueJournal of Hypertension · 2008
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicinePersistence (discontinuity)

Abstract

fetched live from OpenAlex

INTRODUCTION: Little is known about cross-national comparisons of the persistence of antihypertensive medication treatment, trends in persistence, and factors associated with persistence. The aim of this study was to describe and compare patterns of use of antihypertensive drugs in a population of elderly patients in the United States (Pennsylvania), Canada (British Columbia) and the Netherlands. METHODS: A retrospective cohort study of Medicare enrollees in a state pharmacy assistance programme in Pennsylvania (USA), residents from British Columbia (Canada) and residents from the Netherlands registered in the PHARMO database was conducted. Each population included patients 65 years and older who were initiated on blood pressure-lowering treatment between 1 January 1998 and 31 December 2003 and who had continuous follow-up for at least 365 days. In these populations, the proportion of patients with at least 180 consecutive days without medication available (non-persistence) were identified as were predictors of non-persistence using Cox proportional hazards. RESULTS: A total of 9664 Medicare enrollees (USA), 25 377 residents from British Columbia and 24 603 residents from the Netherlands were evaluated. During the first year after the initiation of treatment, the percentage of patients with at least 180 days without medication was 23.3% in Pennsylvania, 23.4% in British Columbia and 24.0% in the Netherlands. After 6 years, these percentages increased to 41.1, 36.3 and 38.2%, respectively. Factors associated with non-persistence were different between the three countries. CONCLUSION: Despite differences in factors associated with persistence, non-persistence patterns are strikingly similar in all three populations. This suggests that the problem of non-persistence transcends international boundaries.

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)0.001
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 score0.088
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.148
GPT teacher head0.310
Teacher spread0.162 · 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

Citations46
Published2008
Admission routes1
Has abstractyes

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