Assessment of Condition and Medication Knowledge Gaps Among Atrial Fibrillation Patients: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Patient education facilitates construction of a correct illness representation, improves beliefs about medications, and improves knowledge, factors that have been associated with better adherence. Objective: Our objective was to characterize the published literature about atrial fibrillation (AF) patients’ disease and medication knowledge to identify knowledge gaps and misconceptions to inform AF patient education strategies. Methods: Following PRISMA guidelines, we searched PubMed, EMBASE, CINAHL, and PsychINFO from inception to May 2018 for studies that assessed AF patients’ knowledge about their condition and medications. For quantitative studies, we extracted the proportion of participants who provided correct answers to the questions asked about their condition, medications, or risk of stroke. We classified data for related questions into knowledge domains. A random-effects meta-analysis was conducted for each knowledge domain. A domain was considered a knowledge gap if the pooled mean proportion of participants who demonstrated knowledge of it was ≤50%, regardless of CI. Qualitative data were summarized narratively. Results: A total of 21 studies were included. AF- and stroke-related knowledge gaps and misconceptions included the following: AF can be asymptomatic, AF can predispose to heart failure, women are at a higher risk of stroke, the definition of ischemic stroke, and patients’ awareness of their diagnosis. Medication-related knowledge gaps were antithrombotic-drug interactions, antithrombotic-food interactions, vitamin K content of foods, the term INR (international normalized ratio) and its interpretation, and the required actions in case of a missed dose. Conclusion and Relevance: This systematic review identified several AF patient knowledge gaps about their condition and its treatment that can inform the development of AF patient education programs.
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 | 0.030 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".