P3616optimal INR level to prevent stroke and bleeding in patients with rheumatic mitral stenosis and atrial fibrillation
Bibliographic record
Abstract
Background: Even though guidelines recommend the target INR level between 2.0 and 3.0 for patients with atrial fibrillation (AF), the evidences in subgroup of patient with valvular AF are limited and the optimal INR level has not been clarified thoroughly as yet, especially in Asian population where studies in patents with nonvalvular AF have shown that the INR level as low as 1.5 could be optimal to prevent thromboembolism without increasing major hemorrhage. Purpose: To determine the optimal INR level to prevent stroke and bleeding in patients with rheumatic mitral stenosis and AF who are receiving warfarin. Methods: This is a retrospective study which enrolled consecutive patients with the ICD coding of rheumatic mitral stenosis and AF who received warfarin at King Chulalongkorn Memorial Hospital between January 1, 2010 and December 31, 2015. The lNR level at the time of the event, the numbers of ischemic stroke and bleeding events were collected. The time density in each INR level, which take consideration of INR level and duration, was used for analysis. The INR range was classified into 6 groups (<1.50, 1.50–1.99, 2.00–2.49, 2.50–2.99, 3.00–3.49 and ≥3.5). The incidence density of ischemic stroke and bleeding events in each INR group was calculated.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".