Advancements and Collaborative Dynamics in the Treatment of Retinoblastoma: A Bibliometric Analysis of Trends and Themes
Bibliographic record
Abstract
This study aimed to provide a comprehensive bibliometric analysis of retinoblastoma treatment, assessing publication trends, influential research, and leading contributors. 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. 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. 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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.267 | 0.608 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.030 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".