Authors’ response: Warfarin, medicinal drugs and road traffic accidents
Bibliographic record
Abstract
We thank Dr Alvarez for his comments in response to our paper [1]. We agree that drink-driving is both concerning and important to reduce as a public health goal, and that appropriate prescribing is a key issue in road safety. The major goal of our study was to assess the validity of a previous finding of a potential hazard for drivers using anticoagulants [2] and provide data for policy makers to make more informed decisions. The extremely high level of use of ‘sedating drugs’ observed in the members of this cohort is definitely concerning and previous research on the same cohort has explored the public health impact of this use [3]. We agree that distance driven could be important for these studies if it differs widely between users and non-users of warfarin. However, the difference in driving between users and non-users would have to be huge for it to disguise an adverse effect of warfarin use in our study. The lack of risk among the exposed suggests that, insofar as there is any extra risk, the current clinical and regulatory framework in Quebec controls it adequately and that no additional regulation is required. In terms of alcohol use, we agree that it would have been ideal to record this information. Unfortunately, the nature of our database (based solely on prescription drugs) made this impractical. As alcohol use is not recommended among warfarin users, it is possible that a lower level of ‘drink-driving’ could explain the protective effects seen in this drug among current users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.030 | 0.029 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
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".