Intravitreal fluocinolone acetonide 0.19 mg Iluvien implant for radiation maculopathy
Bibliographic record
Abstract
OBJECTIVE: To evaluate the efficacy of a long-acting fluocinolone acetonide 0.19 mg Iluvien intravitreal (FAc) implant for the treatment of radiation maculopathy (RM). DESIGN: Retrospective case series. PARTICIPANTS: Seven patients treated for uveal melanoma with palladium-103 plaque brachytherapy who developed RM. METHODS: Patients initially received intravitreal anti-vascular endothelial growth factor (anti-VEGF) therapy. Those who were intolerant or refractory to anti-VEGF were transitioned to the FAc implant. The main outcomes included best-corrected visual acuity (BCVA), intraocular pressure (IOP), and central foveal thickness (CFT) on optical coherence tomography. RESULTS: The median radiation dose to the fovea was 45.9 Gy (mean: 61.8, range: 6.4-135.6). RM onset occurred at a median of 12 months post-radiation. Patients received anti-VEGF therapy for 71 months (mean) before FAc implantation. A median of 1 (mean: 2, range: 1-3) FAc implant was placed per patient, beginning in 2018. Median follow-up was 45 months (mean: 39, range: 14-52). Median BCVA improved from 20/63 (mean: 20/63, range: 20/30-20/160) to 20/50 (mean: 20/50, range: 20/20-20/400). Median CFT decreased from 390 μm (mean: 458, range: 301-845) to 300 μm (mean 427) at 6 months, and 383 μm (mean 413) at the last follow-up. One patient required IOP-lowering treatment, and 3 were able to discontinue anti-VEGF therapy. Two patients required additional FAc implants after 4 years. CONCLUSIONS: FAc implants were an effective treatment for RM patients who were refractory or intolerant to maximal intravitreal anti-VEGF therapy. They achieved positive results in the stabilization of macular edema and visual acuity, as well as reduced the intravitreal anti-VEGF injection burden for some patients.
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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 | 0.000 |
| 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".