Faculty Opinions recommendation of Randomized, controlled trial of TNF-α antagonist in CTL-mediated severe cutaneous adverse reactions.
Bibliographic record
Abstract
BACKGROUND.Cytotoxic T lymphocyte-mediated (CTL-mediated) severe cutaneous adverse reactions (SCARs), including Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN), are rare but life-threatening adverse reactions commonly induced by drugs.Although high levels of CTL-associated cytokines, chemokines, or cytotoxic proteins, including TNF-α and granulysin, were observed in SJS-TEN patients in recent studies, the optimal treatment for these diseases remains controversial.We aimed to evaluate the efficacy, safety, and therapeutic mechanism of a TNF-α antagonist in CTL-mediated SCARs. METHODS.We enrolled 96 patients with SJS-TEN in a randomized trial to compare the effects of the TNF-α antagonist etanercept versus traditional corticosteroids. RESULTS.Etanercept improved clinical outcomes in patients with SJS-TEN.Etanercept decreased the SCORTEN-based predicted mortality rate (predicted and observed rates, 17.7% and 8.3%, respectively).Compared with corticosteroids, etanercept further reduced the skin-healing time in moderate-to-severe SJS-TEN patients (median time for skin healing was 14 and 19 days for etanercept and corticosteroids, respectively; P = 0.010), with a lower incidence of gastrointestinal hemorrhage in all SJS-TEN patients (2.6% for etanercept and 18.2% for corticosteroids; P = 0.03).In the therapeutic mechanism study, etanercept decreased the TNF-α and granulysin secretions in blister fluids and plasma (45.7%-62.5% decrease after treatment; all P < 0.05) and increased the Treg population (2-fold percentage increase after treatment; P = 0.002), which was related to mortality in severe SJS-TEN. CONCLUSIONS.The anti-TNF-α biologic agent etanercept serves as an effective alternative for the treatment of CTL-mediated SCARs.
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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 | 0.006 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.187 | 0.014 |
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".