Biologic therapy in rare eosinophil-associated disorders: remaining questions and translational research opportunities
Bibliographic record
Abstract
Rare eosinophil-associated disorders (EADs), including hypereosinophilic syndrome, eosinophilic granulomatosis with polyangiitis, and eosinophilic gastrointestinal disorders, are a heterogeneous group of conditions characterized by blood and/or tissue hypereosinophilia and eosinophil-related clinical manifestations. Although the recent availability of biologic therapies that directly and indirectly target eosinophils has the potential to dramatically improve treatment options for all EADs, clinical trials addressing their safety and efficacy in rare EADs have been relatively few. Consequently, patient access to therapy is limited for many biologics, and the establishment of evidence-based treatment guidelines has been extremely difficult. In this regard, multicenter retrospective collaborative studies focusing on disease manifestations and treatment responses in rare EADs have provided invaluable data for physicians managing patients with these conditions and helped identify important questions for future translational research. During the Clinical Pre-Meeting Workshop held in association with the July 2023 biennial meeting of the International Eosinophil Society in Hamilton, Ontario, Canada, the successes and limitations of pivotal multicenter retrospective studies in EADs were summarized and unmet needs regarding the establishment of guidelines for use of biologics in rare EADs were discussed. Key topics of interest included (1) clinical outcome measures, (2) minimally invasive biomarkers of disease activity, (3) predictors of response to biologic agents, and (4) long-term safety of eosinophil depletion. Herein, we report a summary of these discussions, presenting a state-of-the-art overview of data currently available for each of these topics, the limitations of the data, and avenues for future data generation through implementation of multidisciplinary and multicenter studies.
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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 | 0.027 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".