Investigation of prevalence of biologic use and discontinuation rates in moderate‐to‐severe psoriasis patients in Newfoundland and Labrador using real‐world data
Bibliographic record
Abstract
Real-world data for psoriasis includes registries, and meta-analyses could help guide biologic therapy choice. The objective was to determine the prevalence and reason for discontinuation of biologic or PD4 inhibitor therapy in patients with moderate-to-severe psoriasis in Newfoundland and Labrador, Canada from 2001 to 2017. A retrospective cohort study was conducted on data collected between 2001 and 2017, to determine the type of biologic therapy or PD4 inhibitor, length of treatment, prevalence of and reason for discontinuation. As multiple patients have been on more than one therapy (ie, an average of 1.8), the 459 patients included in the registry have had a total of 913 exposures to biologic or PD4 inhibitors. The treatment mean time was 37 months (SD 39.95). A total of 180 patients remained on their first biologic of which 75% were male. The average number of biologics per patient was 1.99. The reasons for discontinuation were primary failure (28.5%), adverse events (26.4%), secondary failure (24.3%), patient choice (4.4%), other/unknown in (6.6%), drug withdrawal from market (6.8%), and drug coverage issues (3%). The most common reasons for discontinuation of biologics or PD4 inhibitors include primary failure, adverse events, and secondary failures. Males were more likely to remain on their first biologic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".