Immunomodulatory treatment and surgical management of idiopathic uveitis and juvenile idiopathic arthritis-associated uveitis in children: a French survey practice
Bibliographic record
Abstract
BACKGROUND: Surgeries for idiopathic uveitis and juvenile idiopathic arthritis-associated uveitis in children are complex because of the high risk of inflammatory postoperative complications. There is no consensus about treatment adaptation during the perioperative period. The objectives of this study are to report the therapeutic changes made in France and to determine whether maintaining or stopping immunosuppressive therapies is associated with an increased risk of surgical site infection or an increased risk of uveitis or arthritis flare-up. METHODS: We conducted a retrospective cohort study between January 1, 2006 and December 31, 2018 in six large University Hospitals in France. Inclusion criteria were chronic idiopathic uveitis or chronic uveitis associated with juvenile idiopathic arthritis under immunosuppressive therapies at the time of the surgical procedure, operated before the age of 16. Data on perioperative treatments, inflammatory relapses and post-operative infections were collected. RESULTS: A total of 76 surgeries (42% cataract surgeries, 30% glaucoma surgeries and 16% posterior capsule opacification surgeries) were performed on 37 children. Adaptation protocols were different in the six hospitals. Immunosuppressive therapies were discontinued in five cases (7%) before surgery. All the children in the discontinuation group had an inflammatory relapse within 3 months after surgery compared to only 25% in the other group. There were no postoperative infections. CONCLUSIONS: The results of this study show varying practices between centres. The benefit-risk balance seems to favour maintaining immunosuppressive therapies during surgery. Further studies are needed to determine the optimal perioperative treatments required to limit post-operative inflammatory relapses.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".