TREATMENT COMPLETION AND IMPLEMENTATION BY PATIENTS INITIATING TICAGRELOR
Bibliographic record
Abstract
BACKGROUND: In secondary prevention of adverse events and death following acute coronary syndrome, patients may benefit from adhering to a ticagrelor treatment. OBJECTIVES: The authors assessed the proportion of new ticagrelor users who completed 12 months of treatment, explored the factors associated with treatment completion and, among the completers, evaluated the 12-month treatment implementation. METHODS: A retrospective administrative health database inception cohort study was conducted in a population that included Quebec residents ≥18 years of age who initiated ticagrelor between January 1, 2012 and March 31, 2014. A patient still on ticagrelor at the end of the 12-month period after treatment initiation was considered to have completed the treatment. Factors associated with treatment completion were identified using log-binomial regression. Implementation was assessed using the proportion of days covered (PDC). RESULTS: Of the 3,600 patients, 2,235 (62.1%) completed 12 months of treatment. The patients who were more likely to complete their treatment included those who had visited a general practitioner, had a percutaneous coronary intervention, used a statin or fibrate, and those who used an antihypertensive drug during the year preceding the ticagrelor treatment initiation. Older patients, those with atrial fibrillation, those who had ≥ 6 physician visits and those who used an anticoagulant were less likely to complete the 12-month treatment. The median PDC was 96.2%. CONCLUSION: Treatment completion might be improved. Among patients who completed the treatment, implementation was high. The factors associated with completion could help to identify patients who might benefit from interventions that aim to optimize treatment completion.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".