Guideline-related barriers to optimal prescription of oral anticoagulants in primary care.
Bibliographic record
Abstract
Guidelines provide recommendations for antithrombotic treatment to prevent stroke in people with atrial fibrillation, but oral anticoagulant prescriptions in Dutch primary care are often discordant with these recommendations. Suboptimal guideline features (i.e. format and content) have been suggested as a potential explanatory factor for this type of discordance. Therefore, we systematically appraised features of the Dutch general practitioners' (NHG) atrial fibrillation guideline to identify guidelinerelated barriers that may hamper its use in practice. We appraised the guideline's methodological rigour and transparency using the Appraisal of Guidelines, Research and Evaluation (AGREE) II tool. Additionally, we used the Guideline Implementability Appraisal (GLIA) tool to assess the key recommendations on oral anticoagulant prescription. The editorial independence of the guideline group scored highly (88%); scores for other aspects of the guideline's methodological quality were acceptable, ranging from 53% for stakeholder involvement to 67% for clarity of presentation. At the recommendation level, the main implementation obstacles were lack of explicit statements on the quality of underlying evidence, lack of clarity around the strength of recommendations, and the use of ambiguous terms which may hamper operationalisation in electronic systems. Based on our findings we suggest extending stakeholder involvement in the guideline development process, standardising the layout and language of key recommendations, providing monitoring criteria, and preparing electronic implementation parallel with guideline development. We expect this to contribute to optimising the NHG atrial fibrillation guideline, facilitating its implementation in practice, and ultimately to improving antithrombotic treatment and stroke prevention in people with atrial fibrillation.
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 | 0.146 | 0.520 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".