Content, Accuracy and Quality of Warfarin Patient Information.
Bibliographic record
Abstract
Abstract Background: Warfarin is a very common, narrow therapeutic index drug which requires clear understanding by patients in order to ensure its safe use. Patients are largely educated about warfarin through written information sheets provided by pharmacies. It is critical that these sheets provide all of the essential information about warfarin use in a format that is accurate and understandable. Objective: The goals of this study were to assess the content and accuracy of warfarin information sheets provided to patients by community pharmacies as well as to assess the reading level of each information sheet. Methods: A reference standard for warfarin patient information was developed, based on a formal survey of a national group of thrombosis health professionals. Warfarin patient information sheets, distributed by community pharmacies, were compared to the reference standard. Reading level was assessed using two standardized instruments, the Flesch-Kincaid scale and SMOG scale, and compared to the recommended Grade 5 to 6 level for patient information sheets. Patient information booklets, distributed by the producers of warfarin, were also evaluated. Results: Six different warfarin patient information sheets were distributed by 98% of the community pharmacies in Ontario. On average, these sheets included only 44% of the recommended essential or important knowledge items (range 20–52%), with considerable variability in the content of specific items. Each sheet also contained incorrect information. The average reading level of the information sheets was Grade 11 (range Grade 7 to above Grade 12). On average, the pharmaceutical booklets included 71% of the essential or important information items (range 58–84%). Conclusions: Patients prescribed warfarin are likely to receive information sheets from a community pharmacy that contain less than half of the information content important to their safe use of warfarin as well as statements that are misleading or incorrect. Furthermore, comprehension of these sheets is limited by a reading level substantially higher than recommended. All six of the information sheets reviewed were derived from an American drug information database and therefore, generalized applicability of these results exists. The information that patients receive about warfarin must be improved and we suggest that a national standard be used.
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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 | 0.008 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".