Drug Insurance and Psoriasis Severity: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: Prescription drug insurance in Canada is constituted of a patchwork of public and private insurance plans. The type of drug insurance may have a negative impact on access to treatment for patients covered by public plans compared with private plans. Objectives: In patients with psoriasis treated with advanced therapy in public vs private drug insurance groups, we compared: (1) psoriasis severity scores when an advanced therapy was prescribed, (2) psoriasis severity scores at follow-up, (3) treatment response, and (4) delay between prescription and first dose of advanced therapy. Methods: This unicentric, retrospective cohort study included patients suffering from psoriasis treated by advanced therapy, dermatologist-prescribed between September 2015 and August 2019, in a tertiary academic care center in Québec City, Canada. Data were collected from medical records. Results: Patients treated with an advanced therapy for psoriasis covered under the provincial public drug insurance plan (n = 78) and under a private drug plan (n = 93) did not differ regarding the studied outcomes. Patients’ characteristics differed between groups. Patients in the public group were older (P < .0001), more socioeconomically deprived (P < .05), and more likely to benefit from compassion from the industry to access a prescribed medication free of charge (P < .0001) compared with patients from the privately insured group. Discussion: The high prevalence of compassionate programs from the industry in the public insurance group (42% vs 14%), and the high prevalence of psoriasis on difficult-to-treat areas (face, genitalia, and/or palmoplantar areas) in our cohort (85.4%) may mask differences in access to advanced therapy between the two groups. Conclusions: Prescribers of advanced therapy can be reassured, as we found no inequality in access or care based on patients’ drug insurance coverage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".