Automated Author ProfileLee, R.
Lee, R.
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: 1.6 (sum of 4 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
No description available
Authors
- Dai, F. ;
- Howard, A.W. ;
- Halverson, S. ;
- Orell-Miquel, J. ;
- Palle, E. ;
- Isaacson, H. ;
- Fulton, B. ;
- Price, E.M. ;
- Plotnykov, M. ;
- Rogers, L.A. ;
- Valencia, D. ;
- Paragas, K. ;
- Greklek-McKeon, M. ;
- Gomez Barrientos, J. ;
- Knutson, H.A. ;
- Petigura, E.A. ;
- Weiss, L.M. ;
- Lee, R. ;
- Brinkman, C.L. ;
- Huber, D. ;
- Stefansson, G. ;
- Masuda, K. ;
- Giacalone, S. ;
- Lu, C.X. ;
- Kite, E.S. ;
- Hu, R. ;
- Gaidos, E. ;
- Zhang, M. ;
- Rubenzahl, R.A. ;
- Winn, J.N. ;
- Te, Han ;
- Beard, C. ;
- Holcomb, R. ;
- Householder, A. ;
- Gilbert, G.J. ;
- Lubin, J. ;
- Ong J.M.J. ;
- Polanski, A.S. ;
- Saunders, N. ;
- Van Zandt, J. ;
- Yee, S.W. ;
- Zhang, J. ;
- Zink, J. ;
- Holden, B. ;
- Baker, A. ;
- Brodheim, M. ;
- Crossfield I.J.M. ;
- Deich, W. ;
- Edelstein, J. ;
- Gibson, S.R. ;
- Hill, G.M. ;
- Jelinsky, S.R. ;
- Kassis, M. ;
- Laher, R.R. ;
- Lanclos, K. ;
- Lilley, S. ;
- Payne, J.N. ;
- Rider, K. ;
- Robertson, P. ;
- Roy, A. ;
- Schwab, C. ;
- Shaum, A.P. ;
- Sirk, M.M. ;
- Smith, C. ;
- Vandenberg, A. ;
- Walawender, J. ;
- Wang, S.X. ;
- S.-Y.(., Wang ;
- Wishnow, E. ;
- Wright, J.T. ;
- Yeh, S. ;
- Caballero, J.A. ;
- Morales, J.C. ;
- Murgas, F. ;
- Nagel, E. ;
- Reiners, A. ;
- Schweitzer, A. ;
- Tabernero, H.M. ;
- Zechmeister, M. ;
- Spencer, A. ;
- Ciardi, D.R. ;
- Clark, C.A. ;
- Lund, M.B. ;
- Caldwell, D.A. ;
- Collins, K.A. ;
- Schwarz, R.P. ;
- Barkaoui, K. ;
- Watkins, C. ;
- Shporer, A. ;
- Narita, N. ;
- Fukui, A. ;
- Srdoc, G. ;
- Latham, D.W. ;
- Jenkins, J.M. ;
- Ricker, G.R. ;
- Seager, S. ;
- Vanderspek, R.
No description available
Authors
- Kokori, A. ;
- Tsiaras, A. ;
- Edwards, B. ;
- Rocchetto, M. ;
- Tinetti, G. ;
- Bewersdorff, L. ;
- Jongen, Y. ;
- Lekkas, G. ;
- Pantelidou, G. ;
- Poultourtzidis, E. ;
- Wunsche, A. ;
- Aggelis, C. ;
- Agnihotri, V.K. ;
- Arena, C. ;
- Bachschmidt, M. ;
- Bennett, D. ;
- Benni, P. ;
- Bernacki, K. ;
- Besson, E. ;
- Betti, L. ;
- Biagini, A. ;
- Brandebourg, P. ;
- Bretton, M. ;
- Brincat, S.M. ;
- Calo, M. ;
- Campos, F. ;
- Casali, R. ;
- Ciantini, R. ;
- Crow, M.V. ;
- Dauchet, B. ;
- Dawes, S. ;
- Deldem, M. ;
- Deligeorgopoulos, D. ;
- Dymock, R. ;
- Eenmae, T. ;
- Evans, P. ;
- Esseiva, N. ;
- Falco, C. ;
- Ferratfiat, S. ;
- Fowler, M. ;
- Futcher, S.R. ;
- Gaitan, J. ;
- Grau Horta, F. ;
- Guerra, P. ;
- Hurter, F. ;
- Jones, A. ;
- Kang, W. ;
- Kiiskinen, H. ;
- Kim, T. ;
- Laloum, D. ;
- Lee, R. ;
- Lomoz, F. ;
- Lopresti, C. ;
- Mallonn, M. ;
- Mannucci, M. ;
- Marino, A. ;
- Mario, J.-C. ;
- Marquette, J.-B. ;
- Michelet, J. ;
- Miller, M. ;
- Mollier, T. ;
- Molina, D. ;
- Montigiani, N. ;
- Mortari, F. ;
- Morvan, M. ;
- Mugnai, L.V. ;
- Naponiello, L. ;
- Nastasi, A. ;
- Neito, R. ;
- Pace, E. ;
- Papadeas, P. ;
- Paschalis, N. ;
- Pereira, C. ;
- Perroud, V. ;
- Phillips, M. ;
- Pintr, P. ;
- Pioppa, J.-B. ;
- Popowicz, A. ;
- Raetz, M. ;
- Regembal, F. ;
- Rickard, K. ;
- Roberts, M. ;
- Rousselot, L. ;
- Rubia, X. ;
- Savage, J. ;
- Sedita, D. ;
- Shave-Wall, D. ;
- Sioulas, N. ;
- Skolnik, V. ;
- Smith, M. ;
- St-Gelais, D. ;
- Stouraitis, D. ;
- Strikis, I. ;
- Thurston, G. ;
- Tomacelli, A. ;
- Tomatis, A. ;
- Trevan, B. ;
- Valeau, P. ;
- Vignes, J.-P. ;
- Vora, K. ;
- Vrastak, M. ;
- Walter, F. ;
- Wenzel, B. ;
- Wright, D.E. ;
- Zibar, M.
Background: Identification of low-income women with the rare but serious risk of hereditary cancer and their referral to appropriate services presents an important public health challenge. We report the results of formative research to reach thousands of women for efficient identification of those at high risk and expedient access to free genetic services. External validity is maximized by emphasizing intervention fit with the two end-user organizations who must connect to make this possible. This study phase informed the design of a subsequent randomized controlled trial. Methods: We conducted a randomized controlled pilot study (n = 38) to compare two intervention models for feasibility and impact. The main outcome was receipt of genetic counseling during a two-month intervention period. Model 1 was based on the usual outcall protocol of an academic hospital genetic risk program, and Model 2 drew on the screening and referral procedures of a statewide toll-free phone line through which large numbers of high-risk women can be identified. In Model 1, the risk program proactively calls patients to schedule genetic counseling; for Model 2, women are notified of their eligibility for counseling and make the call themselves. We also developed and pretested a family history screener for administration by phone to identify women appropriate for genetic counseling. Results: There was no statistically significant difference in receipt of genetic counseling between women randomized to Model 1 (3/18) compared with Model 2 (3/20) during the intervention period. However, when unresponsive women in Model 2 were called after 2 months, 7 more obtained counseling; 4 women from Model 1 were also counseled after the intervention. Thus, the intervention model that closely aligned with the risk program’s outcall to high-risk women was found to be feasible and brought more low-income women to free genetic counseling. Our screener was easy to administer by phone and appeared to identify high-risk callers effectively. The model and screener are now in use in the main trial to test the effectiveness of this screening and referral intervention. A validation analysis of the screener is also underway. Conclusion: Identification of intervention strategies and tools, and their systematic comparison for impact and efficiency in the context where they will ultimately be used are critical elements of practice-based research.
Authors
- Joseph, G. ;
- Kaplan, C. ;
- Luce, J. ;
- Lee, R. ;
- Stewart, S. ;
- Guerra, C. ;
- Pasick, R.
Background: Identification of low-income women with the rare but serious risk of hereditary cancer and their referral to appropriate services presents an important public health challenge. We report the results of formative research to reach thousands of women for efficient identification of those at high risk and expedient access to free genetic services. External validity is maximized by emphasizing intervention fit with the two end-user organizations who must connect to make this possible. This study phase informed the design of a subsequent randomized controlled trial. Methods: We conducted a randomized controlled pilot study (n = 38) to compare two intervention models for feasibility and impact. The main outcome was receipt of genetic counseling during a two-month intervention period. Model 1 was based on the usual outcall protocol of an academic hospital genetic risk program, and Model 2 drew on the screening and referral procedures of a statewide toll-free phone line through which large numbers of high-risk women can be identified. In Model 1, the risk program proactively calls patients to schedule genetic counseling; for Model 2, women are notified of their eligibility for counseling and make the call themselves. We also developed and pretested a family history screener for administration by phone to identify women appropriate for genetic counseling. Results: There was no statistically significant difference in receipt of genetic counseling between women randomized to Model 1 (3/18) compared with Model 2 (3/20) during the intervention period. However, when unresponsive women in Model 2 were called after 2 months, 7 more obtained counseling; 4 women from Model 1 were also counseled after the intervention. Thus, the intervention model that closely aligned with the risk program’s outcall to high-risk women was found to be feasible and brought more low-income women to free genetic counseling. Our screener was easy to administer by phone and appeared to identify high-risk callers effectively. The model and screener are now in use in the main trial to test the effectiveness of this screening and referral intervention. A validation analysis of the screener is also underway. Conclusion: Identification of intervention strategies and tools, and their systematic comparison for impact and efficiency in the context where they will ultimately be used are critical elements of practice-based research.
Authors
- Joseph, G. ;
- Kaplan, C. ;
- Luce, J. ;
- Lee, R. ;
- Stewart, S. ;
- Guerra, C. ;
- Pasick, R.