Validation of patient‐reported warfarin dose in a prospective incident cohort study
Bibliographic record
Abstract
PURPOSE: Inconsistencies in the definition and the collection of warfarin dosing data could lead to bias in observational, clinical, and pharmacogenetic studies. The present study aims to assess the concordance between patient-reported and prescribed warfarin doses among new warfarin users in the Quebec Warfarin Cohort (QWC) study. METHODS: Demographic, clinical, and lifestyle data were collected at cohort entry and each three months during a 1-year follow-up period among a subgroup of 219 patients from the prospective QWC study. We evaluated the differences between reported and prescribed warfarin doses overall and at each follow-up period. Concordance was tested in a multivariate generalized linear mixed model and allowed to vary from 95% to 105% of the prescribed dose. RESULTS: Overall, there was no significant difference between reported and prescribed warfarin doses (p>0.05, Pearson coefficient=0.969, power=100%). There was also no significant difference across each of four timepoints tested (p>0.05). We found that 84.0% of the reported warfarin doses were concordant with the prescribed doses. Having a history of myocardial infarction was significantly associated with a low concordance (OR=0.494; CI 95%: 0.286-0.852). CONCLUSION: In our population, we found that patient-reported warfarin dose and prescribed warfarin dose were comparable for the conduct of observational and clinical studies as well as for the validation and implementation of warfarin dosing algorithms. Moreover, the effect was similar whether measured in new-onset users of warfarin and after up to 12 months of use.
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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 | 0.043 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".