Choroidal Neovascularization Associated With Birdshot Chorioretinopathy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Patients with birdshot chorioretinopathy (BCR) may develop visual compromise due to choroidal neovascularization (CNV), and few series address management strategies in the anti-vascular endothelial growth factor (VEGF) era. The purpose of this study was to describe the clinical outcomes of combination anti-VEGF and immunosuppressive therapy for CNV associated with BCR. PATIENTS AND METHODS: Retrospective, interventional case series. Patients with BCR from two tertiary uveitis and retina practices were reviewed. Patients with CNV in association with BCR were identified and reviewed in detail. Clinical features, treatments utilized (ie, anti-VEGF injections, immunosuppressive therapy), and functional and structural outcomes over long-term follow-up were recorded. Outcomes measured included Snellen visual acuity, spectral-domain optical coherence tomography macular thickness during treatment, number and type of anti-VEGF injections, the need for initiation or escalation of immunosuppression, and incidence of CNV in macula-involved versus macula-sparing BCR. RESULTS: Four of 36 BCR patients were diagnosed with choroidal neovascularization (11%). Identification of CNV in all patients prompted treatment with intravitreal anti-VEGF injections and an increase or initiation of local or systemic immunosuppression. Mean Snellen visual acuity improved from 20/60 to 20/30 at final follow-up (P = .02). Mean central subfield thickness improved from 443 μ to 254 μ (P = .04). CNV in association with BCR occurred at a rate of 0.11 events per patient-year (95% CI, 0.02-0.31) in macula-involved BCR versus zero events/patient-year in macula-spared BCR (95% CI, 0-0.058; P = .009). CONCLUSION: Anti-VEGF therapy was effective for the treatment of CNV in BCR patients. A combination of systemic or local immunosuppression and anti-VEGF therapy may be useful in the management of CNV associated with BCR. [Ophthalmic Surg Lasers Imaging Retina. 2016;47:450-457.].
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".