Advancements and Collaborative Dynamics in the Treatment of Retinoblastoma: A Bibliometric Analysis of Trends and Themes
Bibliographic record
Abstract
AIM: This study aimed to provide a comprehensive bibliometric analysis of retinoblastoma treatment, assessing publication trends, influential research, and leading contributors. METHODS: The research was conducted using the Web of Science Core Collection, focusing on retinoblastoma treatment from January 1, 1941, to June 13, 2024. Bibliometric analysis were conducted using Microsoft Excel, VOSviewer, CiteSpace, and the Bibliometrics R package. RESULTS: The analysis identified 5,674 documents. The United States led in research output and citation impact, followed by China and Europe. The University of Toronto was the most prolific institution (488 articles). International collaborations accounted for 18.54% of publications. David H. Abramson was the most prolific author (139 articles), followed closely by C.L. Shields (100 articles). Keyword analysis revealed three major thematic clusters: (1) molecular mechanisms and oncogenesis, (2) cell cycle regulation and experimental models, and (3) clinical management and therapeutic strategies. Recent hotspots included intraarterial chemotherapy, melphalan, treatment resistance, risk stratification, and tumor biology. Retinoblastoma research centers on molecular mechanisms, cell cycle regulation, and clinical management. CONCLUSION: Advances in intraarterial chemotherapy, risk assessment, and molecular insights are improving survival and quality of life. Greater emphasis on real-world, multicenter, and international studies is needed to advance personalized care.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.112 | 0.155 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".