The effect of low-dose oral vitamin K supplementation on INR stability in patients receiving warfarin
Bibliographic record
Abstract
The anticoagulant effect of warfarin is influenced by variations in vitamin K intake. Concomitant use of daily low-dose oral vitamin K (LDVK) and warfarin may improve INR stability. We hypothesise that administration of LDVK improves INR control. To test this hypothesis we performed a multi-centre, placebo-controlled, randomised trial conducted at four university-affiliated hospitals in Canada. Patients on chronic warfarin therapy received oral vitamin K 150 mcg daily or a matching placebo for a total of six months after a one-month run in period. The primary outcome was a comparison of mean time in therapeutic range (TTR) in LDVK and placebo group during a six-month-period. The secondary outcome was number of INR excursions <1.5 or >4.5. There was no significant difference in the final TTR between the two groups (65.1 % vs 66 %, p =0.8). Mean TTR in both LDVK and placebo groups were statistically increased compared with prior to the study. The number of INR excursions were significantly decreased in the LDVK group (9.4 % and 5.4 %, absolute difference [pre- minus post-] = 4 %, 95 % CI, 2 to 6 %, p-value <0.001). We conclude that LDVK administration did not increase mean TTR, but did decrease the number of INR excursions. The observed improvement in mean TTR in both groups suggests that more attentive monitoring of warfarin therapy, rather than LDVK, was responsible for the improvement in TTR observed. The reduced excursions suggest that LDVK did reduce extreme INR variation. The study is registered at www.ClinicalTrial.gov# NCT00990158.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".