Experience Always Wins the Day: Measure of Warfarin Anticoagulation Efficiency in the Internal Medicine Clinic
Bibliographic record
Abstract
Background: In the real-life setting of a specialized anticoagulation clinic we think that the patient ’ s INRs fall within the therapeutical range most of the time. Our study aimed to assess the time spent by patients treated by Warfarin with INR between 2.0 and 3.0, as well the time spent between 1.5 and 3.5. We chose this new range (1.5 - 3.5) because we think that it is safe and completely acceptable in real-life situation, and requires only minor adjustments to return to treatment goal of 2.0 to 3.0. Methods: A single-center, retrospective observational study was conducted at Hotel-Dieu Hospital, between June 2010 and December 2011. Inclusion criteria were (1) to be treated for venous thromboembolic event with Warfarin, and (2) to have at least 3 INR measurements during the study period. Results: The median duration of follow-up was 281 days with a total number of INR values of 2,553. Median proportion of time at target INR (2.0 - 3.0) was 68.9%. This proportion increased to 98.7% between 1.5 and 3.5. There was a single recurrent thrombosis event, 6 minor bleeding episodes and 10 major bleeding episodes. The majority of major bleeding episodes were caused by gastrointestinal bleeding. Conclusions: We demonstrated that patients followed at a specialized anticoagulation clinic spend on average 68.9% of their time within the therapeutical range of INR and 98.7% of their time within very safe and effective INR values. Indeed, Warfarin is still a valuable treatment for thromboembolic event and a very competitive drug. doi: http://dx.doi.org/10.4021/jh100e
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".