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Record W2108234139 · doi:10.1002/pds.1414

Parabolas of medication use and discontinuation after myocardial infarction—are we closing the treatment gap?

2007· article· en· W2108234139 on OpenAlexafffund
Marie Hudson, Hugues Richard, Louise Pilote

Bibliographic record

VenuePharmacoepidemiology and Drug Safety · 2007
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersMcGill University
KeywordsDiscontinuationMedicineMedical prescriptionMyocardial infarctionInternal medicineStatinPharmacoepidemiologyPharmacology

Abstract

fetched live from OpenAlex

PURPOSE: Little is known on the use of evidence-based medications in patients with acute myocardial infarction (AMI) across all ages. We undertook this study to describe the patterns of prescription and discontinuation of anti-platelet agents, beta-blockers, angiotensin converting enzyme (ACE) inhibitors and/or angiotensin receptor blockers (ARBs) and statins in all patients post-AMI. METHODS: Using population-based administrative databases, patients with an AMI between 1999 and 2004 (21 494 men and 13 241 women) were identified. Rates of prescriptions after discharge and time to discontinuation of the study drugs were computed for various age groups. RESULTS: The proportion of patients prescribed a study drug increased throughout the study period. In 2003-2004, 90% of patients were prescribed an anti-platelet agent, 77% a beta-blocker, 72% a statin and 70% an ACE inhibitor and/or an ARB within 30 days of discharge from their AMI. However, the rates of discontinuation increased significantly during follow-up and, in men, reached 27% by 2 years and 42% by 5 years for beta-blockers. The rates of discontinuation of all four study drugs had a parabolic shape with the youngest and oldest patients having the highest rates. CONCLUSIONS: The use of evidence-based drugs for patients after AMI is increasing. However, efforts aimed at closing the treatment gap may be mitigated by high rates of discontinuation, especially in patients at the extremes of the age spectrum.

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)0.003
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 score0.160
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.065
GPT teacher head0.386
Teacher spread0.321 · 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

Citations43
Published2007
Admission routes2
Has abstractyes

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