Automated Author ProfileDamato, B.
Damato, B.
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.8 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Background/Aims: Current retinoblastoma staging systems do not adequately describe the disease, especially in eyes with multiple tumors. The aims of this study were to develop methods for documenting individual tumors and to score disease burden over time. Methods: A coding system was devised to describe each tumor according to affected eye, meridian, anteroposterior location, activity, growth pattern, type of seed, and treatment. A scoring system for quantifying disease burden was developed, taking account of tumor number, size, spread, and secondary effects on the eye. Results: Our coding system allowed contemporaneous tumor documentation, producing datasets that enabled generation of fundus diagrams, Kaplan-Meier curves, and tables summarizing disease progression in individual tumors and eyes. Our data showed disparities between ocular and tumor documentation, e.g., indicating earlier tumor development in the left eye but younger age at presentation if disease was worse in the right eye. Actuarial rates of local treatment failure were lower when individual tumors were analyzed than when data were reported in terms of whole eyes. Conclusion: Our methods for documenting individual retinoblastomas have facilitated the review of patients’ progress in our routine practice and may provide data that could be used to refine retinoblastoma classifications in the future.
Authors
- Damato, B. ;
- Afshar, A.R. ;
- Everett, L. ;
- Banerjee, A. ;
- Hetts, S.W.
Background/Aims: Current retinoblastoma staging systems do not adequately describe the disease, especially in eyes with multiple tumors. The aims of this study were to develop methods for documenting individual tumors and to score disease burden over time. Methods: A coding system was devised to describe each tumor according to affected eye, meridian, anteroposterior location, activity, growth pattern, type of seed, and treatment. A scoring system for quantifying disease burden was developed, taking account of tumor number, size, spread, and secondary effects on the eye. Results: Our coding system allowed contemporaneous tumor documentation, producing datasets that enabled generation of fundus diagrams, Kaplan-Meier curves, and tables summarizing disease progression in individual tumors and eyes. Our data showed disparities between ocular and tumor documentation, e.g., indicating earlier tumor development in the left eye but younger age at presentation if disease was worse in the right eye. Actuarial rates of local treatment failure were lower when individual tumors were analyzed than when data were reported in terms of whole eyes. Conclusion: Our methods for documenting individual retinoblastomas have facilitated the review of patients’ progress in our routine practice and may provide data that could be used to refine retinoblastoma classifications in the future.
Authors
- Damato, B. ;
- Afshar, A.R. ;
- Everett, L. ;
- Banerjee, A. ;
- Hetts, S.W.