Inflammatory Choroidal Neovascular Membranes: Clinical Profile, Treatment Effectiveness, and Visual Prognosis
Bibliographic record
Abstract
Purpose . To characterise a sample of patients with inflammatory choroidal neovascularization (I‐CNV), including clinical profile, underlying aetiology and its course, treatments performed, associated clinical response, and visual prognosis. Methods . Retrospective analysis of patients with a diagnosis of I‐CNV followed at the Ophthalmology Department of Centro Hospitalar Universitário de São João (CHUSJ). Clinical and visual outcomes were classified according to the difference in visual acuity after treatment. Results . Twenty eyes from 17 patients were analysed (11 female and 6 male patients, mean age 41.90 ± 16.457 years at CNV diagnosis). Punctate inner choroidopathy/multifocal choroiditis was the predominant inflammatory aetiology (10 patients, 58.82%). Median follow‐up time was 46 months (range 10 to 188 months). Neovascularization was treated with intravitreal anti‐VEGF injections (bevacizumab, aflibercept, and ranibizumab), and inflammation with anti‐inflammatory/immunosuppressive therapy (oral, intravenous, and/or intravitreal corticosteroids; oral cyclosporine or methotrexate). Intravitreal anti‐VEGF agents had a median number of 7.00 injections (IQR, 4.25 to 29.00). Visual acuity among 20 eyes had a mean gain of 15.10 ± 12.998 ETDRS letters after anti‐VEGF treatment ( p = 0.000051). According to our classification, 16 had an improved outcome (80.00%), 3 had a stable outcome (15.00%), and 1 had a worsened visual outcome (5.00%). In addition, 13 eyes (65.00%) had a final VA equal to or greater than 65 letters. Recurrence was seen in 3 eyes (15.00%). Complications included cataract (6 patients) and ocular hypertension (4 patients). Conclusion . A combined approach with anti‐VEGF agents and anti‐inflammatory therapy was effective in I‐CNV treatment, and an overall good visual prognosis was attainable. Intensive follow‐up was fundamental in the management of both the primary inflammatory and secondary neovascular conditions.
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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 | 0.001 |
| 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".