Real-world adherence to oral anticoagulants in atrial fibrillation patients: a study protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Atrial fibrillation (AF) is one of the leading causes of cerebrovascular mortality and morbidity. Oral anticoagulants (OACs) have been shown to reduce the incidence of cardioembolic stroke in patients with AF, adherence to treatment being an essential element for their effectiveness. Since the release of the first non-vitamin K antagonist oral anticoagulant, several observational studies have been carried out to estimate OAC adherence in the real world using pharmacy claim databases or AF registers. This systematic review aims to describe secondary adherence to OACs, to compare adherence between OACs and to analyse potential biases in OAC secondary adherence studies using databases. METHODS AND ANALYSIS: We searched on PubMed, SCOPUS and Web of Science databases (completed in 26 September 2018) to identify longitudinal observational studies reporting days' supply adherence measures with OAC in patients with AF from refill databases or AF registers. The main study endpoint will be the percentage of patients exceeding the 80% threshold in proportion of days covered or the medication possession ratio. Two reviewers will independently screen potential studies and will extract data in a structured format. A random-effects meta-analysis will be carried out to pool study estimates. The risk of bias will be assessed using the Newcastle-Ottawa Scale for observational studies and we will also assess some study characteristics that could affect days' supply adherence estimates. ETHICS AND DISSEMINATION: This systematic review using published aggregated data does not require ethics approval according to Spanish law and international regulations. The final results will be published in a peer-review journal and different social stakeholders, non-academic audiences and patients will be incorporated into the diffusion activities. PROSPERO REGISTRATION NUMBER: CRD42018095646.
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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 | 0.053 | 0.071 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.022 | 0.027 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.042 | 0.004 |
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".