Ranibizumab for idiopathic epiretinal membranes: A retrospective case series
Bibliographic record
Abstract
PURPOSE: To study the effect of intravitreal ranibizumab on idiopathic epiretinal membranes (ERMs). METHODS: A retrospective cohort study on a consecutive series of ranibizumab intravitreal injections for epiretinal membranes was performed. Four cases were identified by reviewing a claims database linked to electronic medical records. All patients received a total of three 0.05 mg/0.05 ml ranibizumab intravitreal injections at a monthly interval. The primary outcome measure was the final best-corrected visual acuity (BCVA) at the end of the injection series, and the final central macular thickness (CMT). RESULTS: All four patients completed 3 months follow-up after the last ranibizumab injection. The mean baseline CMT was 509 microns (SD = 111). A trend was noticed for reduction in CMT (Δ = 41 microns) P = 0.08. Three patients improved by one line in their BCVA. The remaining patient maintained the same BCVA. No complications were noted. CONCLUSION: In this study, intravitreal injection of ranibizumab marginally reduced retinal thickness in four patients with minimal improvement in visual acuity. No safety concerns were noticed. Further basic science and clinical studies may be warranted to assess the role of vascular endothelial growth factor and the effect of ranibizumab on idiopathic epiretinal membranes.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".