| Title |
A Data-Driven Algorithm Integrating Clinical and Laboratory Features for the Diagnosis and Prognosis of Necrotizing Enterocolitis
|
|---|---|
| Published in |
PLOS ONE, February 2014
|
| DOI | 10.1371/journal.pone.0089860 |
| Pubmed ID | |
| Authors |
Jun Ji, Xuefeng B. Ling, Yingzhen Zhao, Zhongkai Hu, Xiaolin Zheng, Zhening Xu, Qiaojun Wen, Zachary J. Kastenberg, Ping Li, Fizan Abdullah, Mary L. Brandt, Richard A. Ehrenkranz, Mary Catherine Harris, Timothy C. Lee, B. Joyce Simpson, Corinna Bowers, R. Lawrence Moss, Karl G. Sylvester |
| Abstract |
Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting. |
X Demographics
The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 4 | 36% |
| United States | 4 | 36% |
| Panama | 1 | 9% |
| Unknown | 2 | 18% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 55% |
| Scientists | 4 | 36% |
| Practitioners (doctors, other healthcare professionals) | 1 | 9% |
Mendeley readers
The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 82 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 13 | 16% |
| Student > Master | 12 | 15% |
| Student > Bachelor | 11 | 13% |
| Researcher | 10 | 12% |
| Other | 5 | 6% |
| Other | 10 | 12% |
| Unknown | 21 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 26 | 32% |
| Psychology | 6 | 7% |
| Nursing and Health Professions | 5 | 6% |
| Computer Science | 5 | 6% |
| Agricultural and Biological Sciences | 4 | 5% |
| Other | 8 | 10% |
| Unknown | 28 | 34% |