Disciplinary Roots of 300 Top-Ranked Scientific Contributors to Gerontology: From Legacy to Enriching Our Discovery.
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ABSTRACT:
SUBMITTER: Ferraro KF
PROVIDER: S-EPMC9678198 | biostudies-literature | 2022 Nov
REPOSITORIES: biostudies-literature
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Disciplinary Roots of 300 Top-Ranked Scientific Contributors to Gerontology: From Legacy to Enriching Our Discovery.
The journals of gerontology. Series A, Biological sciences and medical sciences 20221101 11
Gerontology is viewed by many as a multidisciplinary field of inquiry, but which disciplines have had the greatest impact on research in the field? Combining data from a composite score incorporating multiple citation indicators with information on the highest degree, we examine the disciplinary origins of the 300 top-ranked scholars in gerontology. Despite efforts for gerontology to be distinct from geriatrics, more than 30 percent of the most influential scholars in gerontology during the past ...[more]
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Project description:Importance:Hospital networks formed around top-ranked cancer hospitals represent an opportunity to optimize complex cancer care in the community. Objective:To compare the short- and long-term survival after complex cancer treatment at top-ranked cancer hospitals and the affiliates of top-ranked hospitals. Design, Setting, and Participants:This cohort study was conducted using data from the unabridged version of the National Cancer Database. Included patients were individuals 18 years or older who underwent surgical treatment for esophageal, gastric, lung, pancreatic, colorectal, or bladder cancer diagnosed between January 1, 2012, and December 31, 2016. Patient outcomes after complex surgical procedures for cancer at top-ranked cancer hospitals (as ranked in top 50 by US News and World Report) were compared with outcomes at affiliates of top-ranked cancer hospitals (affiliation listed in American Hospitals Association survey and confirmed by search of internet presence). Data were analyzed from July through December 2019. Exposures:Undergoing complex cancer treatment at a top-ranked cancer hospital or an affiliated hospital. Main Outcomes and Measures:The association of affiliate status with short-term survival (ie, 90-day mortality) was compared using logistic regression, and the association of affiliate status with long-term survival was compared using time-to-event models, adjusting for patient demographic, payer, clinical, and treatment factors. Results:Among 119?834 patients who underwent surgical treatment for cancer, 79?981 patients (66.7%) were treated at top-ranked cancer hospitals (median [interquartile range] age, 66 [58-74] years; 40?910 [54.9%] men) and 39?853 patients (33.3%) were treated at affiliate hospitals (median [interquartile range] age, 69 [60-77] years; 19?004 [50.0%] men). In a pooled analysis of all cancer types, adjusted perioperative mortality within 90 days of surgical treatment was higher at affiliate hospitals compared with top-ranked hospitals (odds ratio, 1.67 [95% CI, 1.49-1.89]; P?<?.001). Adjusted long-term survival following cancer treatment at affiliate hospitals was only 77% that of top-ranked hospitals (time ratio, 0.77 [95% CI, 0.72-0.83]; P?<?.001). The survival advantage was not fully explained by differences in annual surgical volume, with both long- and short-term survival remaining superior at top-ranked hospitals even after models were adjusted for volume. Conclusions and Relevance:These findings suggest that short- and long-term survival after complex cancer treatment were superior at top-ranked hospitals compared with affiliates of top-ranked hospitals. Further study of cancer care within top-ranked cancer networks could reveal collaborative opportunities to improve survival across a broad contingent of the US population.
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Project description:Rank aggregation (RA), the process of combining multiple ranked lists into a single ranking, has played an important role in integrating information from individual genomic studies that address the same biological question. In previous research, attention has been focused on aggregating full lists. However, partial and/or top ranked lists are prevalent because of the great heterogeneity of genomic studies and limited resources for follow-up investigation. To be able to handle such lists, some ad hoc adjustments have been suggested in the past, but how RA methods perform on them (after the adjustments) has never been fully evaluated. In this article, a systematic framework is proposed to define different situations that may occur based on the nature of individually ranked lists. A comprehensive simulation study is conducted to examine the performance characteristics of a collection of existing RA methods that are suitable for genomic applications under various settings simulated to mimic practical situations. A non-small cell lung cancer data example is provided for further comparison. Based on our numerical results, general guidelines about which methods perform the best/worst, and under what conditions, are provided. Also, we discuss key factors that substantially affect the performance of the different methods.
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