Etanercept in the treatment of palmoplantar pustulosis.
Bibliographic record
Abstract
INTRODUCTION: Palmoplantar pustulosis (PPP) is a chronic, recurrent and difficult to treat skin condition characterized by the presence of pustules, erythema, and hyperkeratosis on palms and soles. METHODS: Fifteen subjects with PPP were randomized (2:1) to receive subcutaneous injections of either etanercept 50 mg or a placebo twice a week for 3 months. All subjects then received the etanercept 50 mg injections twice a week for an additional 3 months. RESULTS: Etanercept was well tolerated by subjects with PPP. The decrease in median Palmoplantar Pustulosis Area and Severity Index (PPPASI) score from baseline to 24 weeks was statistically significant for subjects treated with etanercept for 24 weeks (P = 0.038, n = 10) but not for subjects in the placebo/etanercept cross-over group (P = 0.125, n = 5). Comparison of changes in PPPASI from baseline to week 12 was not statistically significant for subjects assigned to etanercept or to placebo. Some subjects treated with etanercept presented good clinical improvements in PPP severity whereas others showed an increase in PPP severity. CONCLUSION: This study showed that etanercept was well tolerated in subjects with PPP and suggests that some PPP subjects might benefit from etanercept therapy. Larger studies are needed to assess PPP response to etanercept including the influence of smoking and the presence or absence of psoriasis outside palms and soles.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".