Abstract 11847: Genotype-Guided Vitamin K Antagonist Dosing Algorithms Improve Time in Therapeutic Range: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Variability in vitamin K antagonist (VKA) dosing is partially explained by CYP2C9 and VKORC1 polymorphisms. Genotype-guided VKA dosing may improve quality of anticoagulation and outcomes. We performed a meta-analysis to determine whether genotype-guided VKA dosing algorithms decrease a composite of death, thromboembolic events and major bleeding and improve time in therapeutic range (TTR). Methods: We searched MEDLINE, EMBASE, CENTRAL, trial registries and conference proceedings for randomized trials comparing genotype-guided and non genotype-guided VKA dosing algorithms in adults initiating anticoagulation. Data was extracted in duplicate and pooled using a random effects model. Results: We included 12 studies involving 3217 patients. Nine studies reported at least one of the components of the composite outcome (2483 patients, 94 events). Pooled data indicate that the relative risk for the composite outcome was similar in both groups (0.82; 95%CI 0.55,1.22) with no evidence of heterogeneity (×2=5.76, p=0.57, I2=0%). The genotype-guided group had higher TTR (mean difference 4.31; 95% CI 0.35,8.26; heterogeneity ×2=43.31, p Conclusions: Genotype-guided compared with non genotype-guided VKA dosing algorithms did not decrease a composite of death, thromboembolism and major bleeding but resulted in improved TTR. The improvement in TTR appeared to be isolated in the subgroup of studies using a fixed VKA dosing algorithm in the non genotype-guided group.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".