Comparison of international normalized ratio audit parameters in patients enrolled in GARFIELD‐AF and treated with vitamin K antagonists
Bibliographic record
Abstract
Vitamin K antagonist (VKA) therapy for stroke prevention in atrial fibrillation (AF) requires monitoring of the international normalized ratio (INR). We evaluated the agreement between two INR audit parameters, frequency in range (FIR) and proportion of time in the therapeutic range (TTR), using data from a global population of patients with newly diagnosed non-valvular AF, the Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF). Among 17 168 patients with 1-year follow-up data available at the time of the analysis, 8445 received VKA therapy (±antiplatelet therapy) at enrolment, and of these patients, 5066 with ≥3 INR readings and for whom both FIR and TTR could be calculated were included in the analysis. In total, 70 905 INRs were analysed. At the patient level, TTR showed higher values than FIR (mean, 56·0% vs 49·8%; median, 59·7% vs 50·0%). Although patient-level FIR and TTR values were highly correlated (Pearson correlation coefficient [95% confidence interval; CI], 0·860 [0·852-0·867]), estimates from individuals showed widespread disagreement and variability (Lin's concordance coefficient [95% CI], 0·829 [0·821-0·837]). The difference between FIR and TTR explained 17·4% of the total variability of measurements. These results suggest that FIR and TTR are not equivalent and cannot be used interchangeably.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".