Efficacy of combined anti-VEGF and photodynamic therapy for bilateral diffuse uveal melanocytic proliferation
Bibliographic record
Abstract
RATIONALE: Bilateral diffuse uveal melanocytic proliferation (BDUMP) is an extremely rare retinal exudative disease with physical disorders and no established treatment standard. We describe treatment courses in 3 cases of BDUMP. PATIENTS CONCERNS: Three male patients complained active vision loss. One male patient in his 70s (patient 1) was treated with prednisolone, mesalazine, and ciclosporin for hypoplastic anemia and ulcerous colitis. One male patient in his 60s (patient 2) was on prednisolone therapy for adult Still disease. Another male patient in his 70s (patient 3) was on prednisolone therapy for polymyalgia rheumatica, giant cell arteritis, and pancreatic body tumor. DIAGNOSES: Retinal specialists diagnosed these patients with BDUMP based on characteristic fundus findings of multiple red patches and retinal exudate. INTERVENTIONS: Two patients (patients 1 and 2) with poor response to anti-vascular endothelial growth factor (VEGF) monotherapy and/or triamcinolone acetonide sub-Tenon injection were treated with combined anti-VEGF therapy and photodynamic therapy. One patient (patient 3) was treated with 3 rounds of monthly anti-VEGF monotherapy. OUTCOMES: Retinal exudates were resolved in all patients. No recurrence of retinal exudates was observed for at least 10 months, 2 years, or 4 months after the therapy in patients 1, 2, and 3, respectively. However, best-corrected visual acuity of the right eye was low (20/200) compared with that of the left eye (20/22) in patient 2 despite exudate resolution, due to permanent outer retinal damage secondary to long-term retinal exudate. LESSONS SUBSECTIONS: Combined anti-VEGF therapy and photodynamic therapy may be a feasible therapeutic option for treatment-resistant exudate in patients with BDUMP. Early diagnosis of BDUMP and prompt administration of combination therapy are crucial.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".