Adherence to Antithrombotic Therapy for Patients Attending a Multidisciplinary Thrombosis Service in Canada – A Cross-Sectional Survey
Bibliographic record
Abstract
Background: Poor medication adherence puts patients who require antithrombotic therapy at greater risk of complications. We started a multidisciplinary Adult Outpatient Thrombosis Service in 2017 in a Canadian health authority and were interested in the level of medication adherence in the population attending. AimS: The aim of this study is to assess adherence to antithrombotic medications for patients attending a multidisciplinary Thrombosis Service. Methods: We conducted a cross-sectional survey of outpatients seen at the Thrombosis Service between 2017 and 2019 using the 12-item validated Adherence to Refills and Medications Scale (ARMS) to assess adherence to antithrombotic (anticoagulants and antiplatelet) therapy. Linear regression analysis examined the factors associated with adherence to antithrombotic therapy. Results: Of 1058 eligible patients, 53.2% responded to the survey. Seventeen were excluded from the analysis for missing more than 6 responses to the 12 items on the ARMS. About 55% (n = 297) were on direct oral anticoagulants (DOACs), 19% (n = 102) on warfarin, 5.0% (n = 27) on low molecular weight heparin, 3.3% (n = 18) on antiplatelet therapy and 18% (n = 96) were no longer on antithrombotic therapy. Nearly half (47%, n = 253) had taken antithrombotic therapy for 1-5 years while 28% (n = 150) and 25% (n = 137) had taken antithrombotic treatments for <1 and >5 years, respectively. Most patients (87%, n = 475) were ≥50 years and half (51%, n = 277) were male. The mean adherence score was 13.9 (SD±2.2) and 88% (n = 481) of participants were adherent to antithrombotic treatment (ARMS = 12-16). Multivariable linear regression showed that patients with post-graduate education had 0.4% lower adherence to antithrombotic therapy as compared with elementary education (β = 0.0039, p = 0.048). Patients with prior antithrombotic agent use >5 years had 0.5% lower adherence to antithrombotic treatment compared to patients with <1 year (β = 0.0047, p = 0.0244). Conclusion: Self-reported adherence to antithrombotic therapy was high (88%) within a multidisciplinary Thrombosis Service. Patients with advanced education and prolong duration of antithrombotic therapy were more likely to have lower self-reported adherence to antithrombotic treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".