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Record W4409148801 · doi:10.31128/ajgp-02-24-7168

Overall medicine burden for people on lipid-lowering therapy: Cross-sectional analysis of national pharmacy dispensing data

2025· article· en· W4409148801 on OpenAlexaboutno aff
Sally Dunbar, Danny Liew, Jenni Ilomäki, Stella Talic

Bibliographic record

VenueAustralian Journal of General Practice · 2025
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePharmaceutical Benefits SchemeMedical prescriptionPharmacyCohortComorbidityCross-sectional studyQuarter (Canadian coin)Alternative medicineFamily medicineCohort studyEmergency medicineEnvironmental healthIntensive care medicineInternal medicinePharmacologyPathology

Abstract

fetched live from OpenAlex

MEDICINES are increasingly used to control risk factors; lipid-lowering medicines are recommended for cardiovascular disease (CVD) and risk. 1 Since 2016-17, statins have been the most commonly prescribed medicines in Australia 2 and, in 2016, they were prescribed to over 40% of older people. 1 Many other chronic conditions are commonly comorbid with CVD, including diabetes, chronic lung disease, mental health disorder and autoimmune conditions, 3 so patients taking statins are often prescribed multiple additional medicines.Polypharmacy is described as taking five or more concurrent medicines; 4 the polypharmacy rate among US patients with CVD aged 65 years increased from 24% in 2000 to 39% in 2014. 5As medicine number and complexity increase, so do the risks of adverse interactions and events, 4 and reduced patient adherence. 6In Australia, most longer-term medicines have been dispensed in monthly quantities, also necessitating frequent pharmacy visits, although Pharmaceutical Benefits Scheme (PBS) changes in September 2023 have begun to address this with 60-day supply for some items.People incur financial cost for medicines.International research shows that this is associated with reduced adherence, adverse outcomes and increased expenditure for the healthcare system. [7][8]][9] The Australian PBS mitigates cost with a fixed patient co-payment for each prescription dispensed.In 2019 this was capped at $6.60 for low-income and disadvantaged 'concessional' beneficiaries, and at $40.30 for general beneficiaries.The PBS-published Dispensed Price for Maximum Quantity (DPMQ) indicates, for each medicine, the maximum chargeable by pharmacies, including all fees and mark-ups. 10Co-payments from registered family members contribute towards a PBS safety net threshold; in 2019 this was $390 for concessional and $1550.70 for general beneficiaries.After reaching this threshold, co-payments were removed for concessional beneficiaries and reduced to $6.60 for general beneficiaries. 11 The aim of this study was to investigate the largely unknown medicine burden and costs for both general and concessional beneficiaries dispensed with lipid-lowering medicines in Australia.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.216
GPT teacher head0.499
Teacher spread0.283 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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