Genotype-guided versus standard vitamin K antagonist dosing algorithms in patients initiating anticoagulation
Bibliographic record
Abstract
Variability in vitamin K antagonist (VKA) dosing is partially explained by genetic polymorphisms. We performed a meta-analysis to determine whether genotype-guided VKA dosing algorithms decrease a composite of death, thromboembolic events and major bleeding (primary outcome) and improve time in therapeutic range (TTR). We searched MEDLINE, EMBASE, CENTRAL, trial registries and conference proceedings for randomised trials comparing genotype-guided and standard (non genotype-guided) VKA dosing algorithms in adults initiating anticoagulation. Data were pooled using a random effects model. Of the 12 included studies (3,217 patients), six reported all components of the primary outcome of mortality, thromboembolic events and major bleeding (2,223 patients, 87 events). Our meta-analysis found no significant difference between groups for the primary outcome (relative risk 0.85, 95% confidence interval [CI] 0.54-1.34; heterogeneity Χ(²)=4.46, p=0.35, I(²)=10%). Based on 10 studies (2,767 patients), TTR was significantly higher in the genotype-guided group (mean difference (MD) 4.31%; 95% CI 0.35, 8.26; heterogeneity Χ(²)=43.31, p<0.001, I(²)=79%). Pre-specified exploratory analyses demonstrated that TTR was significantly higher when genotype-guided dosing was compared with fixed VKA dosing (6 trials, 997 patients: MD 8.41%; 95% CI 3.50,13.31; heterogeneity Χ(²)=15.18, p=0.01, I(²)=67%) but not when compared with clinical algorithm-guided dosing (4 trials, 1,770 patients: MD -0.29%; 95% CI -2.48,1.90; heterogeneity Χ(²)=1.53, p=0.68, I(²)=0%; p for interaction=0.002). In conclusion, genotype-guided compared with standard VKA dosing algorithms were not found to decrease a composite of death, thromboembolism and major bleeding, but did result in improved TTR. An improvement in TTR was observed in comparison with fixed VKA dosing algorithms, but not with clinical algorithms.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.032 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".