Clinical and genetic factors influencing acenocoumarol dosing
Bibliographic record
Abstract
: Coumadin oral anticoagulants are widely used in multiple clinical scenarios. Their narrow therapeutic range and a dosing strategy based on 'a posteriori' algorithms, pose them as an interesting group for prediction modelling research. Extensive literature explaining the association between clinical and genetic variables with the dose of warfarin have been published. Limited information exists regarding these factors and acenocoumarol dosing. The aim of the study is to explain through clinical/genetic variables, the weekly dose of acenocoumarol necessary for achieving stable anticoagulation status. We performed a cross-sectional study enrolling adults under treatment with acenocoumarol with at least three consecutive INRs between 2 and 3. To explain the association between demographic, clinical and genotype data (VKORC1, CYP2C9 and CYP4F2) and the mean weekly dose of acenocoumarol, we performed a multiple linear regression model. In our cohort, a higher age, the presence of atrial fibrillation, chronic renal failure and VKORC1 haplotype A were associated with a lower mean weekly dose of acenocoumarol. On the other side, a higher weight was associated with a higher weekly dose. Amongst anticoagulated adult patients, VKORC1 genotype and baseline clinical factors can explain acenocoumarol dosing, and therefore, help clinicians while deciding the initial anticoagulant dose.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".