Retinoblastoma – A comprehensive review, update and recent advances
Bibliographic record
Abstract
Retinoblastoma is the most common pediatric ocular malignancy. It is triggered by a biallelic mutation in the RB1 gene or MYCN oncogene amplification. Retinoblastomas can be unilateral (60%-70%) or bilateral (30%-40%); bilateral tumors are always heritable and present at an earlier age as compared to unilateral ones (18-24 months vs. 36 months in India). High prevalence rates, delayed presentation, and inaccessibility to healthcare lead to worse outcomes in developing countries. The past few decades have seen a paradigm change in the treatment of retinoblastomas, shifting from enucleation and external beam radiotherapy to less aggressive modalities for eye salvage. Multimodality treatment is now the standard of care and includes intraarterial or intravenous chemotherapy along with focal consolidation therapies such as transpupillary thermotherapy, cryotherapy, and laser photocoagulation. Intravitreal and intracameral chemotherapy can help in controlling intraocular seeds. Advanced extraocular or metastatic tumors still have a poor prognosis. Genetic testing, counseling, and screening of at-risk family members must be incorporated as essential parts of management. A better understanding of the genetics and molecular basis of retinoblastoma has opened up the path for potential targeted therapy in the future. Novel recent advances such as liquid biopsy, prenatal diagnosis, prognostic biomarkers, tylectomy, and chemoplaque point to promising future directions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".