Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Survival after the onset of congestive heart failure in Framingham Heart Study subjects.
19931.4k citationsKannel Wb et al.Circulationprofile →
The effects of specific medical conditions on the functional limitations of elders in the Framingham Study.
19941.3k citationsAndrew A. Guccione, David T. Felson et al.American Journal of Public Healthprofile →
Stroke risk profile: adjustment for antihypertensive medication. The Framingham Study.
This map shows the geographic impact of Kannel Wb's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kannel Wb with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kannel Wb more than expected).
This network shows the impact of papers produced by Kannel Wb. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kannel Wb. The network helps show where Kannel Wb may publish in the future.
Co-authorship network of co-authors of Kannel Wb
This figure shows the co-authorship network connecting the top 25 collaborators of Kannel Wb.
A scholar is included among the top collaborators of Kannel Wb based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Kannel Wb. Kannel Wb is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Guccione, Andrew A., David T. Felson, Jennifer J. Anderson, et al.. (1994). The effects of specific medical conditions on the functional limitations of elders in the Framingham Study.. American Journal of Public Health. 84(3). 351–358.1295 indexed citations breakdown →
Wb, Kannel, W P Castelli, & Shannon L. Meeks. (1985). Fibrinogen and Cardiovascular Disease. Journal of Cardiopulmonary Rehabilitation. 5(6). 292–292.9 indexed citations
11.
Wb, Kannel. (1981). Mild hypertension as a cardiovascular risk factor.. PubMed. 7(10). 7–14.6 indexed citations
12.
Wb, Kannel, P A Wolf, & Thomas R. Dawber. (1978). Hypertension and cardiac impairments increase stroke risk.. Munich Personal RePEc Archive (Ludwig Maximilian University of Munich). 33(9). 71–3.28 indexed citations
Wb, Kannel. (1974). Role of blood pressure morbidity and mortality. Progress in Cardiovascular Diseases. 27. 5–24.6 indexed citations
16.
Wb, Kannel & Castelli Wp. (1972). The Framingham study of coronary disease in women.. PubMed. 100(5). 173–5 passim.16 indexed citations
17.
Wb, Kannel, et al.. (1965). COMPARISON OF SERUM LIPIDS IN THE PREDICTION OF CORONARY HEART DISEASE. FRAMINGHAM STUDY INDICATES THAT CHOLESTEROL LEVEL AND BLOOD PRESSURE ARE MAJOR FACTORS IN CORONARY HEART DISEASE; EFFECT OF OBESITY AND CIGARETTE SMOKING ALSO NOTED.. PubMed. 48. 243–50.19 indexed citations
18.
Wb, Kannel, Widmer Lk, & Dawber Tr. (1965). [HAZARDS OF CORONARY DISEASE. CONCLUSIONS FOR MEDICAL PRACTICE FROM 10 YEARS OF FRAMINGHAM STUDY].. PubMed. 95. 18–24.6 indexed citations
19.
Tr, Dawber, et al.. (1964). THE PREDICTION OF CORONARY HEART DISEASE.. PubMed. 47. 70–105.38 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.