Management of repository corticotropin injection therapy for non‐infectious uveitis: a Delphi study
Bibliographic record
Abstract
PURPOSE: Diagnosis and management of non-infectious uveitis (NIU), a major cause of blindness worldwide, are challenging. Corticosteroids, the cornerstone of therapy, are not appropriate for long-term use, and while non-biologic and biologic immunomodulators may be used for some patients, data on their efficacy and safety in this population are limited. Repository corticotropin injection (RCI), believed to affect uveitis by multiple mechanisms, has received regulatory approval for treatment of ophthalmic diseases including posterior uveitis, but is not widely used or discussed in guidelines for the management of uveitis and ocular inflammatory diseases. METHODS: The index study employed a modified Delphi process with a panel of 14 US-based ophthalmologists. Consensus recommendations were developed through a series of three questionnaires. Panellists rated statements on a Likert scale from -5 (strongly disagree) to +5 (strongly agree). RESULTS: The Delphi panel provided consensus recommendations on examinations and testing needed for diagnosis, treatment goals, and the use of corticosteroids, as well as the use of non-biologic and biologic immunomodulators. The panel reached consensus that RCI may be considered for posterior and pan-uveitis, and dosing should be individualized for each patient. Dose reduction/discontinuation should be considered for excessive RCI-related toxicity, hyperglycaemia and/or diabetic complications, excessive costs, or remission ≥ 2 years. Patients should be weaned from RCI if uveitis is stable and well controlled. Adverse events during RCI therapy can be managed by appropriate interventions, with dose reduction/discontinuation considered if events are severe or recurrent. CONCLUSIONS: Expert consensus suggests RCI may be an appropriate treatment option for some patients with uveitis when other therapies are ineffective or intolerable.
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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 | 0.082 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".