TREATMENT OF RETINITIS PIGMENTOSA—RELATED CYSTOID MACULAR EDEMA WITH INTRAVITREOUS RANIBIZUMAB
Bibliographic record
Abstract
In Brief Purpose: To report the results of the use of intravitreous ranibizumab for treatment of cystoid macular edema (CME) associated with retinitis pigmentosa (RP). Methods: Intravitreous ranibizumab was used to treat a patient with CME associated with RP who was unable to tolerate oral acetazolamide and did not respond to topical dorzolamide treatment. Visual acuity, clinical examination, and optical coherence tomography (OCT) were performed before and after treatment. Results: Intravitreous ranibizumab improved best-corrected visual acuity (BCVA) and decreased central retinal thickness (CRT) with disappearance of cystic spaces on the OCT. There was recurrence of CME on discontinuation of injections. Conclusion: Intravitreous ranibizumab may be an effective therapy for reducing RP-related CME. Further studies are required to determine whether intravitreous ranibizumab is beneficial in the management of this disease. A 36-year old male with retinitis pigmentosa (RP)-related cystoid macular edema (CME) responded favorably to intravitreous ranibizumab, with resolution of CME and improved visual acuity. Three months after discontinuing treatment, CME recurred. Intravitreous ranibizumab may be an effective therapy for RP-related CME.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".