Nonmelanoma huidkanker tijdens behandeling met TNF-blokkers bij psoriasis en reumatoïde artritis
Bibliographic record
Abstract
To investigate if there was a difference in time to the occurrence of first non-melanoma skin cancer (NMSC) and in the incidence of NMSC between psoriasis patients and rheumatoid arthritis (RA) patients on TNF inhibitors. Prospective observational cohort study. We compared the time to first NMSC (expressed as hazard ratio) and the incidence of NMSC (expressed as incidence ratio) in psoriasis and RA patients in the Netherlands who were treated with TNF inhibitors and had a follow-up of at least one year. Cox regression and Poisson regression analyses were used; both were corrected for confounders (age, gender, disease duration, prior NMSC, duration of anti-TNF and other systemic therapies). The NMSC risk was significantly higher in the psoriasis group (fully adjusted hazard ratio: 6.0 [1.6 - 22.4 95% CI]) and time to first NMSC in psoriasis compared with RA was also shorter (1.6 years and 3.7 years, respectively). The incidence of NMSC was 5.5 times higher in psoriasis patients (2.2 - 13.4 95% CI) than in those with RA. The time to first NMSC was significantly shorter and the incidence of NMSC was significantly higher in psoriasis than in RA. This indicates that disease-related factors such as phototherapy may be important contributing factors to the occurrence of NMSC in psoriasis patients treated with TNF inhibitors
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".