Anti-TNFα therapy in the management of psoriasis: experience of a state referral center
Bibliographic record
Abstract
BACKGROUND: Psoriasis is a chronic immune-mediated disease, characterized by increased levels of TNFα. Anti-TNFα agents have revolutionized the treatment of severe psoriasis by targeting an important molecule involved in its pathogenesis. OBJECTIVES: We report the experience of a state referral center that uses anti-TNFα agents for psoriasis. METHODS: We conducted a retrospective case series. Seventy-four out of 120 patients met the inclusion criteria. Clinical and laboratory data was analyzed using the chi-squared, Wicoxon and McNemar's tests. Associations were considered statistically significant when p-value<0.05. RESULTS: Forty-one subjects (55.40%) were male, with a mean age of 47.69 ± 14.99 years. Median disease duration and pre-treatment PASI were 14.0 months (IQR 9.0-20.0), and 13.55 points (IQR 8.5-20.32). Sixty patients (81.10%) had arthropathic psoriasis. Forty-six subjects (62.20%) had comorbidities; the most frequent was dyslipidemia (25.70%). In 55.40% of patients, insufficient response to conventional therapies was the principal indication for using anti-TNFα drugs. Clinical improvement occurred in 93.20% of cases, and the post-treatment PASI median was 0.0 points (IQR 0.0-0.0). Adverse effects occurred in 6.80% of patients. Infections and elevation of transaminases occurred in 28.40% and 8.10% of cases, respectively. CONCLUSION: Post-treatment reduction in PASI was satisfactory and the occurrence of adverse effects was minor, mostly mild infusion effects and local reactions at drug administration sites.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".