P34 The incidence and prevalence of psoriasis in Newfoundland and Labrador, Canada
Bibliographic record
Abstract
Abstract The Canadian province of Newfoundland and Labrador (NL) is recognized as a founder population of English and Irish descent, with an approximate population of 525 000 people. The incidence and prevalence of psoriasis in the NL population have not been formally studied, although provincial dermatologists acknowledge an increased burden of psoriasis, compared with other regions in Canada. Our objective was to examine the incidence and prevalence of psoriasis in NL and the variation by age, gender, geographic region, and time. Digital Health, Newfoundland and Labrador Health Services provided Provincial Electronic Health Records that were used to identify patients with psoriasis using International Classification of Diseases 9th revision (ICD-9) code 696 from 2001 to 2020. The prevalence of psoriasis was 6830 per 100 000 people (3801 women, 3029 men), and incidence was 365.4 per 100 000 person-years [95% confidence interval (CI), 298.0–432.9]; 395.9 (329.5–462.2) per 100 000 person-years for women and 334.2 (263.5–404.9) per 100 000 person-years for men. The peak age group for incident cases was 55–59 years, suggesting that the late onset of psoriasis is higher than in most Western European and North American populations. There was significant geographic variation in the prevalence of psoriasis, as 20 of the 234 communities with a population over 200 people had a psoriasis prevalence between 0 and 3% and nine communities with psoriasis prevalence over 30%. It is likely that regional founder effects most likely account for the high prevalence of psoriasis in selected communities, although environmental influences cannot be discounted. The NL populations had a high incidence and prevalence of psoriasis, with significant regional variations in prevalence.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".