What affects anticoagulation control in patients taking warfarin?
Bibliographic record
Abstract
BACKGROUND: The ageing population is taking an increasing number of both prescribed and non-prescribed medication. Little is known of the potential for adverse drug reactions between these. Warfarin is a commonly prescribed medication, well known for its potential to cause serious adverse reactions in combination with many prescription medicines. It has been suggested that herbal medicines such as garlic, either as a dietary supplement or in cooking, may also interact with warfarin, resulting in poor international normalised ratio (INR) control. AIM: To determine whether, for patients who take garlic as well as warfarin, the proportion of the INR tests in range is lower than in comparable patients who do not take garlic. DESIGN OF THE STUDY: Retrospective study of patients taking prescribed warfarin. SETTING: Primary care practices in Somerset and Devon. METHOD: Three controls (not taking garlic) matched for age, sex, and general practice were compared with each patient self-reporting taking garlic as a supplement. INR results were assessed for the preceding 12 months. Potentially confounding factors were considered, for example diabetes mellitus; all prescribed medication; any bleeding episodes. RESULTS: No evidence was found to suggest that garlic consumption either as a supplement or in cooking is associated with more frequent haemorrhagic complications or less control of INR. Poor INR control may, however, be associated with taking larger numbers of prescription medicines, particularly during prescription changes. CONCLUSION: Further research would be warranted into whether increased INR monitoring is needed when drug changes are made. These data render clinically significant interactions between warfarin and garlic intake unlikely.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".