Anticoagulation in atrial fibrillation. Is there a gap in care for ambulatory patients?
Bibliographic record
Abstract
OBJECTIVE: Atrial fibrillation (AF) substantially increases risk of stroke. Evidence suggests that anticoagulation to reduce risk is underused (a "care gap"). Our objectives were to clarify measures of this gap in care by including data from family physicians and to determine why eligible patients were not receiving anticoagulation therapy. DESIGN: Telephone survey of family physicians regarding specific patients in their practices. SETTING: Nova Scotia. PARTICIPANTS: Ambulatory AF patients not taking warfarin who had risk factors that made anticoagulation appropriate. MAIN OUTCOME MEASURES: Proportion of patients removed from the care gap; reasons given for not giving the remainder anticoagulants. RESULTS: Half the patients thought to be in the care gap had previously unknown contraindications to anticoagulation, lacked a clear indication for anticoagulation, or were taking warfarin. Patients' refusal and anticipated problems with compliance and monitoring were among the reasons for not giving patients anticoagulants. CONCLUSION: Adding data from primary care physicians significantly narrowed the care gap. Attention should focus on the remaining reasons for not giving eligible patients anticoagulants.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".