Retinoma: An overview
Bibliographic record
Abstract
ABSTRACT Retinoma, also referred to as retinocytoma, is a benign manifestation of biallelic retinoblastoma gene (RB1) inactivation. Genetic or epigenetic loss of retinoblastoma protein in maturing cone precursors induces genomic instability which leads to upregulation of senescence‐associated p16INK4a and p130, resulting in non‐proliferative retinoma. When senescence pathways fail and genetic instability accumulates to a critical level through altered gene copies of oncogenes and tumor suppression genes, transformation into RB1−/‐ retinoblastoma occurs. Thus, the management of retinoma involves frequent ophthalmic examination and imaging to monitor the size and characteristics of the tumor, ensure stability, and rule out malignant transformation. Key ophthalmoscopic features of retinoma often include a translucent whitish‐gray retinal mass, calcification, retinal pigment epithelial alterations with well‐defined margins, located typically around the lesion, as well as a zone of chorioretinal atrophy. This review aims to provide a comprehensive overview of this non‐malignant tumor drawing from current understanding of its molecular genetics, clinical characteristics, diagnostic modalities, differential diagnosis, management, and prognosis. A deeper understanding of retinoma could offer valuable insights into how retinoblastoma develops and oncogenesis more broadly, paving the way for improved strategies to prevent and treat this malignant tumor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".