Hydroxychloroquine prescription trends and predictors for excess dosing per recent ophthalmology guidelines
Bibliographic record
Abstract
BACKGROUND: Hydroxychloroquine (HCQ) retinopathy may be more common than previously recognized; recent ophthalmology guidelines have revised recommendations from ideal body weight (IBW)-based dosing to actual body weight (ABW)-based dosing. However, contemporary HCQ prescribing trends in the UK remain unknown. METHODS: We examined a UK general population database to investigate HCQ dosing between 2007 and 2016. We studied trends of excess HCQ dosing per ophthalmology guidelines (defined by exceeding 6.5 mg/kg of IBW and 5.0 mg/kg of ABW) and determined their independent predictors using multivariable logistic regression analyses. RESULTS: Among 20,933 new HCQ users (78% female), the proportions of initial HCQ excess dosing declined from 40% to 36% using IBW and 38% to 30% using ABW, between 2007 and 2016. Among these, 47% of women were excess-dosed (multivariable OR 12.52; 95% CI 10.99-14.26) using IBW and 38% (multivariable OR 1.98; 95% CI,1.81-2.15) using ABW. Applying IBW, 37% of normal and 44% of obese patients were excess-dosed; however, applying ABW, 53% of normal and 10% of obese patients were excess-dosed (multivariable ORs = 1.61 and 0.1 (reference = normal); both p < 0.01). Long-term HCQ users showed similar excess dosing. CONCLUSION: A substantial proportion of HCQ users in the UK, particularly women, may have excess HCQ dosing per the previous or recent weight-based guidelines despite a modest decline in recent years. Over half of normal-BMI individuals were excess-dosed per the latest guidelines. This implies the potential need to reduce dosing for many patients but also calls for further research to establish unifying evidence-based safe and effective dosing strategies.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".