Time in therapeutic range: Warfarin anticoagulation for atrial fibrillation in a community-based practice.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of an outpatient, nurse-administered warfarin anticoagulation protocol for the treatment of atrial fibrillation, and to identify clinical or biographical data that predict poor international normalized ratio control. DESIGN: Retrospective cohort study. SETTING: St Paul Family Health Network in Brantford, Ont. PARTICIPANTS: A total of 150 patients with nonvalvular atrial fibrillation. MAIN OUTCOME MEASURES: Time in therapeutic range (TTR) for each patient and for the clinic overall. The groups of patients above and below a target TTR of 60% were compared by stepwise binomial logistic regression. RESULTS: A time-weighted average TTR for the clinic was determined to be 58.76%, based on 183 452 patient-days taking warfarin. The regression analysis did not find a statistically significant association between TTR and any predictors. A trend indicating a 5-fold increase in the odds of inadequate anticoagulation was observed in current smokers (odds ratio of 4.71; 95% CI 0.97 to 22.93). CONCLUSION: Compared with data from prospective randomized trials and meta-analysis, the anticoagulation protocol employed at the St Paul Family Health Network produced an average TTR near the lower end of the target threshold. Current smokers might be at greater risk of being below this target.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".