Estimated Frequency of Psychodermatologic Conditions in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Psychodermatologic disorders are difficult to identify and treat. Knowledge about the prevalence of these conditions in dermatological practice in Canada is scarce. This hampers our ability to address potential gaps and establish optimal care pathways. OBJECTIVES: To provide an estimate of the frequencies of psychodermatologic conditions in dermatological practice in Alberta, Canada. METHODS: Two administrative provincial databases were used to estimate the prevalence of potential psychodermatological conditions in Alberta from 2014 to 2018. Province-wide dermatology claims data were examined to extract relevant International Classification of Disease codes as available. Claims were linked with pharmacy dispensation data to identify patients who received at least 1 psychoactive medication within 90 days of the dermatology claim. RESULTS: Of 243 963 patients identified, 28.6% had received at least 1 psychotropic medication (mean age: 47.9 years; 67.5% female). Rates of concurrent psychotropic medications were highest for pruritus and related conditions (46.7%), followed by urticaria (44.5%) and hyperhidrosis (32.8%). Among patients with psychotropic medications, rates of antidepressants were highest (56.3%), followed by anxiolytics (37.1%). Across billing codes, besides hyperhidrosis (71.2%), diseases of hair (61.4%) and psoriasis (59.1%) had the highest rates of antidepressant dispensations. Patients with atopic dermatitis had the highest rates for anxiolytic prescriptions (54.3%). CONCLUSION: In a 5-year window, more than a quarter of the identified dermatology patients in Alberta received at least 1 psychotropic medication, pointing to high rates of potential psychodermatologic conditions and/or concurrent mental health issues in dermatology. Diagnostic and care pathways should include a multidisciplinary approach to better identify and treat these conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".