Glucocorticoids and the emerging importance of T cell subsets in steroid refractory diseases
Bibliographic record
Abstract
Lauren P. Schewitza, Richard W. J. Lee*a, Colin M. Dayanb & Andrew D. Dickca Academic Unit of Ophthalmology, Department of Clinical Science at South Bristol, University of Bristol, Bristol, United Kingdomb Henry Wellcome Laboratories for Integrative Neuroscience and Endocrinology, Department of Clinical Science at South Bristol, University of Bristol, Bristol, United Kingdomc Department of Cellular and Molecular Medicine, University of Bristol, Bristol, United KingdomAddress for Correspondence: Richard Lee, Department of Clinical Science at South Bristol, University of Bristol, School of Medical Sciences, University Walk, Bristol, BS8 1TD, United Kingdom; E-mail: richard.lee@bristol.ac.ukGlucocorticoids remain the first-line treatment for a range of autoimmune and allergic diseases. However, 30% of patients fail to achieve disease control at tolerable systemic doses and continue to have an increased immune response with poor clinical outcome. This steroid refractory (SR) phenotype has previously been attributed to enhanced expression of inactive glucocorticoid receptor isoforms and cytokine-mediated suppression of glucocorticoid (GC) signaling, in particular by interleukin-2. These mechanisms are discussed, with emphasis on recent evidence for the role of the CD4+CD25int and GC-induced T regulatory cell subsets in perpetrating SR disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".