Influence of Gender on the Persistence of Different Tumor Necrosis Factor Inhibitor Treatments in Patients With Psoriatic Arthritis
Bibliographic record
Abstract
To the Editor: Psoriatic arthritis (PsA) affects men and women equally, although it is known that important differences exist between the sexes. We have read the recently published paper, “Women with psoriatic arthritis experience higher disease burden than men: findings from a real-world survey in the United States and Europe,” by Gossec et al.1 The authors highlight that in patients with similar PsA disease activity and treatment, women experienced greater disease impact than men. However, this issue is currently not resolved and further research is needed in this area.2 To this end, our group evaluated the efficacy of tumor necrosis factor inhibitors (TNFi) in patients diagnosed with PsA, and the influence of the patient’s gender. A multicenter and observational study was conducted in patients with PsA receiving treatment with etanercept (ETN), adalimumab (ADA), golimumab (GOL), and certolizumab pegol (CZP) between February 1, 2021, and February 1, 2022. Patients were evaluated using the Disease Activity Index for Psoriatic Arthritis (DAPSA) and its cut-off points at baseline and at 52 weeks or when the patient stopped treatment. Minimal disease activity (MDA) and 12-item … Address correspondence to Dr. C. García-Porrúa, Hospital Universitario Lucus Augusti Rheumatology, C/Dr. Ulises Romero, nº 1, Lugo, Lugo 27003, Spain. Email: Carlos.Garcia.Porrua{at}sergas.es.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".