CYSTOID MACULAR EDEMA IN BIRDSHOT RETINOCHOROÏDITIS
Bibliographic record
Abstract
PURPOSE: To assess the long-term efficacy and safety of treatments for cystoid macular edema in birdshot retinochoroïditis. METHODS: Observational retrospective study of 142 HLA-A29-positive patients with cystoid macular edema; the main outcome was the optical coherence tomography intraretinal cysts resolution. RESULTS: During the mean follow-up of 75 months (12-178), 61.3% of patients were successfully treated using 1 to 3 treatment steps, while the others needed more steps. At 6 months, there were no significant effects on ME for anti-TNF (tumor necrosis factor) and IVIg (immunoglobulin) in contrast to antimetabolites (OR 1.98), systemic GCS (glucocorticosteroids), CsA (cyclosporine A) and tocilizumab (odds ratio closed to 2.7), intraocular injected GCS (odds ratio of 4.2), and interferon (odds ratio of 4.4). The percentages of therapeutic success trend to decrease from the initial three treatment steps to the subsequent treatment steps, for systemic GCS (84% to 70%), for anti-TNF (42% to 33%), and for CsA (71% to 33%); the success percentages did not decrease for injected GCS (83% to 89%). Macular edema recurrence occurred with the highest percentage for injected GCS (86.8%, P = 0.01) and the lowest for tocilizumab (10.5%, P = 0.001). Interferons-α and tocilizumab were associated with the lowest prednisone daily doses. CONCLUSION: The classical uveitic cystoid macular edema therapeutic algorithm could be adapted to birdshot retinochoroïditis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".