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Record W4296781881 · doi:10.1111/jch.14567

Laboratory testing and antihypertensive medication adherence following initial treatment of incident, uncomplicated hypertension: A real‐world data analysis

2022· article· en· W4296781881 on OpenAlexafffundabout
Reed F. Beall, Alexander A. C. Leung, Amity E. Quinn, Charleen Salmon, Tayler Scory, Lauren Bresee, Paul E. Ronksley

Bibliographic record

VenueJournal of Clinical Hypertension · 2022
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsUniversity of Calgary
FundersM.S.I. Foundation
KeywordsMedicinePersistence (discontinuity)Medical prescriptionRetrospective cohort studyCohortInternal medicineCohort studyEmergency medicinePediatrics

Abstract

fetched live from OpenAlex

In this study on medication adherence among newly diagnosed patients with uncomplicated, incident hypertension, we conducted a retrospective cohort study using available administrative and laboratory data from April 1, 2012 to March 31, 2017 in Alberta, Canada to understand the extent to which baseline laboratory assessment and/or subsequent follow-up was associated with persistence with antihypertensive therapy. We determined the frequency of baseline and follow-up testing and compared the rates of medication persistence by patient-, neighbourhood-, and treatment-related factors. Of 103 232 patients with newly diagnosed, uncomplicated hypertension who filled their first prescription within our study timeframe, 52.5% were non-persistent within 6 months. Persistent patients were more often female and residing in neighbourhoods with higher social status (with exception to rurality). Aside from older age, the strongest predictor of persistence was performance of laboratory testing related to hypertension with an apparent effect in which higher levels of medication persistence were seen with more frequent laboratory testing. We concluded that medication persistence was far from optimal, dropping off considerably after 6 months for more than half of patients. Medication persistence is a substantial barrier to realizing the full societal benefits of antihypertensive treatment. Ongoing follow up with patients, including laboratory testing, may be a critical component of better long term treatment persistence.

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)0.003
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 score0.080
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.315
GPT teacher head0.443
Teacher spread0.127 · 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

Citations8
Published2022
Admission routes3
Has abstractyes

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