Dermatologists’ Adherence to the Latest Recommendations for Screening of Hydroxychloroquine Retinopathy in Saudi Arabia: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Hydroxychloroquine (HCQ) has been used to manage many inflammatory skin conditions. Nevertheless, retinopathy continues to be its most significant adverse effect. The American Academy of Ophthalmology (AAO) recommends baseline ophthalmologic screening in the first year of HCQ treatment. However, a recent study found an inadequate awareness of the recommendations. Furthermore, limited data are available regarding the implementation of the recommendations among dermatologists. OBJECTIVE: The aim of this study was to assess dermatologists' adherence to recommendations pertaining to their current practice regarding HCQ toxicity detection. METHODS: A self-administrated questionnaire was distributed between February 2 and May 4, 2018, among members of the Saudi Society of Dermatology. The questionnaire comprised demographic-related questions and questions pertaining to each physician's routine practice about the follow-up of HCQ-treated patients. RESULTS: A total of 76 dermatologists completed the questionnaire. We achieved a response rate of 62.54%. More than half (43/76, 56%) of the dermatologists were male. Furthermore, more than half (41/76, 53%) of them reported treating 1 to 3 patients with HCQ during the last year. Furthermore, two-thirds (47/76, 61%) of them reported screening patients before initiating HCQ treatment. Regarding follow-up recommendations, 59% (45/76) of dermatologists reported yearly after starting treatment for no-risk patients, whereas 94% (72/76) reported "yearly within 5 years of treatment" for at-risk patients. Data were considered significant at P<.05. All analyses were performed using SPSS, version 20 (IBM). CONCLUSIONS: Dermatologists in Saudi Arabia are not well informed about some aspects of the latest recommendations regarding screening for HCQ toxicity in terms of tests, follow-up timing, cessation of the drug, and causative agents. Therefore, we recommend conducting more studies in Saudi Arabia to determine the adherence of more physicians to the AAO recommendations. Furthermore, patient education regarding HCQ toxicity and increased patient awareness are recommended for effective and safe HCQ 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".