Detection of novel therapies using a <scp>multi‐national</scp>, <scp>multi‐institutional</scp> registry of cutaneous <scp>immune‐related</scp> adverse events and management
Bibliographic record
Abstract
BACKGROUND: Cutaneous immune-related adverse events (cirAEs) remain a prevalent and common sequelae of immune checkpoint inhibitor (ICI) therapy, often necessitating treatment interruption and prolonged immune suppression. Treatment algorithms are still poorly defined, based on single-institution case reports without adequate safety assessments, and subject to publication bias. METHODS: Data in this registry were collected through a standardized REDCap form distributed to dermatologists via email listserv. RESULTS: Ninety-seven cirAEs were reported from 13 institutions in this registry. Topical and systemic steroids were the most common treatments used; however, targeted treatment matched to disease morphology was identified at numerous sites. Novel cirAE therapy uses that to our knowledge have not been previously described were captured including tacrolimus for the treatment of follicular, bullous, and eczematous eruptions and phototherapy for eczematous eruptions. Moreover, further evidence of cirAE treatment applications sparsely described in literature were also captured in this study including dupilumab and rituximab for bullous eruptions, phototherapy for lichenoid and psoriasiform eruptions, and acitretin for psoriasiform eruptions, among others. No serious adverse events were reported. Numerous targeted therapeutics including dupilumab, rituximab, and psoriasis biologics, among others, were associated with a cirAE grade improvement of ≥2 grades in every patient treated. CONCLUSION: This study suggests that a multi-institutional registry of cirAEs and management is not only feasible but that the information collected can be used to detect, evaluate, and rigorously assess targeted treatments for cirAEs. Further expansion and modification to include treatment progression may allow for sufficient data for specific treatment recommendations to be made.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".