Decisional algorithm to prescribe vitamin K antagonist in geriatric patients with atrial fibrillation
Bibliographic record
Abstract
Preventing atrial fibrillation (AF) complications relies mainly on anticoagulant therapy. Still it is difficult to prescribe vitamin K antagonists (VKA) in geriatric patients with AF. In order to improve anticoagulation decision in this disease, we set up an algorithm. Charts of all patients with AF hospitalized between February and May 2012 were reviewed. Patients treated with anticoagulation for another indication (venous thromboembolism disease, prosthetic valve) were excluded. Algorithm was built-up with 6 criteria (past bleeding with VKA, autonomy (GIR score), MMSE score, risk of falls, co-morbidities index). Each criterion had a score (0, 0.5, 1 point) according to an intensity scale (light, moderate, high). The final algorithm composite score led to the prescription or not of VKA. Patients were followed-up during 6 months after discharge. One hundred and fifty-three patients were included, mean age 86.1 ± 5.6 years; 67.3% had a GIR score ≤3, 70.6% MMSE score < 23, and 83.7% a moderate risk of falls. According to the algorithm, 92 patients (60.1%) had a VKA prescription. Prescription was significantly less prescribed in the oldest old (p=0.02). Follow-up showed 4 bleeding events without any link with VKA prescription. Thirty-four patients died (22.2%), among 24 (34.4%) who did not have VKA (p=0.005). The algorithm improves VKA prescription according to an objective evaluation and probably prevents the prescription in the patients with the worse short term prognosis.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".