Stability and clinical determinants of long‐term warfarin therapy: a retrospective study
Bibliographic record
Abstract
Anticoagulation (AC) with vitamin K antagonists (VKAs), notably warfarin, is the therapy of choice for the prevention and treatment of thrombotic complications. However, stability of treatment with VKAs remains a daily challenge with bleeding a common side effect of the treatment. To this day, causal factors for bleeding episodes remain ill defined. Here we present a study that aimed to characterize the stability and complications of patients undergoing AC therapy, and identify associated risk factors. The study included 1003 patients treated at the AC clinic of a Montreal university hospital between April 1 2008 and March 31 2009. Socio‐demographic and medical information were extracted from medical charts. Stability of anticoagulation was based on the International Normalized Ratio (INR). Half of patients spent 85% of the time within their respective therapeutic range and factors investigated explained only ~ 8 % of the treatment variance, with intensity of treatment, patient's health condition and non‐compliance to treatment contributing the most. Nutritional information was largely missing from the medical charts and could not be assessed as part of this study. Complications were few and minor in nature. This study highlights the lack of attention paid to nutritional factors, notably those related to vitamin K status, which could contribute to the unexplained treatment variance. Supported by CIHR.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".