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Record W1765682497 · doi:10.1185/03007995.2015.1096242

Adherence to non-vitamin-K-antagonist oral anticoagulant medications based on the Pharmacy Quality Alliance measure

2015· article· en· W1765682497 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Medical Research and Opinion · 2015
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsGroup for Research in Decision Analysis
Fundersnot available
KeywordsApixabanMedicineDabigatranRivaroxabanMedicare Part DVitamin K antagonistInternal medicineWarfarinPharmacyMedical prescriptionEmergency medicineAtrial fibrillationPharmacologyFamily medicinePrescription drug

Abstract

fetched live from OpenAlex

BACKGROUND: CMS Star Ratings help inform beneficiaries about the performance of health and drug plans. Medication adherence is currently weighted at nearly half of a Part D plan's Star Ratings. Including the adherence to non-vitamin-K-antagonist oral anticoagulants (NOACs) as a measure in the Star Ratings program may increase a plan's incentives to improve patient adherence. OBJECTIVE: To assess the adherence to medication of patients who used the NOACs rivaroxaban, dabigatran, or apixaban in 2014 based on the Pharmacy Quality Alliance (PQA) adherence measure. METHODS: Healthcare claims from the Humana database between July 2013 and December 2014 were analyzed. Adult patients with ≥2 dispensings of NOAC agents in 2014, at least 180 days apart, with >60 days of supply, and ≥180 days of continuous enrollment prior to the index NOAC were identified. The PQA measure was calculated as the percentage of patients who had a proportion of days covered (PDC) ≥0.8. Multivariate logistic regression analyses were also conducted adjusting for baseline confounders. RESULTS: A total of 11,095 rivaroxaban, 6548 dabigatran, and 3532 apixaban users were identified. Based on the PQA adherence measure (PDC ≥0.8), a significantly higher proportion of rivaroxaban users (72.7%) was found to be adherent compared to dabigatran (67.2%: p < 0.001) and apixaban (69.5%: p < 0.001) users. Compared to apixaban users, the adjusted likelihood of being adherent was significantly higher for rivaroxaban users (unadjusted OR [95% CI]: 1.17 [1.08-1.27], p < 0.001; adjusted OR [95% CI]: 1.20 (1.10-1.31), p < 0.001) and significantly lower for dabigatran users (unadjusted OR [95% CI]: 0.90 [0.82-0.98], p = 0.019; adjusted OR [95% CI]: 0.85 [0.77-0.93], p < 0.001). LIMITATIONS: Limitations of the study are potential inaccuracies in claims data, possible change in patterns over time, and the impossibility of knowing whether all supplied tablets were taken. CONCLUSION: Using the PQA's adherence measure, rivaroxaban users were found to have significantly higher adherence compared to apixaban and dabigatran users.

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.

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)0.006
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.727
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.532
GPT teacher head0.564
Teacher spread0.032 · 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