Pharmacy hydroxyurea education materials for patients with sickle cell disease: An environmental scan and assessment of accuracy
Bibliographic record
Abstract
BACKGROUND: Hydroxyurea (HU) remains a cornerstone of sickle cell disease (SCD) therapy; however, its use is limited by poor patient adherence owing to concerns about side effects. Pharmacies routinely provide patients with handouts about HU, which, we hypothesized, contain inaccuracies that may contribute to negative patient perceptions of HU. METHODS: We used a systematic approach to collect and review patient information handouts (PIHs) on HU from pharmacies in Ontario, Canada. PIHs were evaluated according to: i. Number of inaccurate statements, ii. Percentage of essential statements based on comparison with a reference standard PIH developed by the Canadian Haemoglobinopathy Association (CanHaem), and iii. Reading level. RESULTS: PIHs were collected from 98% of chain and community pharmacies registered in Ontario. All PIHs contained inaccurate statements, most frequently relating to the risk of developing cancer. Only 33% of PIHs identified SCD as an indication for HU use. Pharmacy PIHs contained 45% of the essential statements present within the CanHaem HU PIH, neglecting to mention use of HU for management of SCD and benefits of HU in preventing SCD complications. Moreover, the reading level across pharmacy PIHs was 1.8 grades higher than that advised for written patient education materials. CONCLUSION: Patients who are prescribed HU are likely to be provided with PIHs that contain inaccuracies that are weighted toward the risks of HU therapy and run contrary to published literature. This study identifies a gap in the care of patients with SCD and an opportunity to improve the quality of HU PIHs to help patients make well-informed decisions about their health.
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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 | 0.035 | 0.168 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".