| Title |
Improving Prediction of Surgical Site Infection Risk with Multilevel Modeling
|
|---|---|
| Published in |
PLOS ONE, May 2014
|
| DOI | 10.1371/journal.pone.0095295 |
| Pubmed ID | |
| Authors |
Lauren Saunders, Marion Perennec-Olivier, Pascal Jarno, François L’Hériteau, Anne-Gaëlle Venier, Loïc Simon, Marine Giard, Jean-Michel Thiolet, Jean-François Viel |
| Abstract |
Surgical site infection (SSI) surveillance is a key factor in the elaboration of strategies to reduce SSI occurrence and in providing surgeons with appropriate data feedback (risk indicators, clinical prediction rule). |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 1% |
| Unknown | 67 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 19 | 28% |
| Student > Master | 7 | 10% |
| Student > Bachelor | 6 | 9% |
| Other | 6 | 9% |
| Researcher | 6 | 9% |
| Other | 12 | 18% |
| Unknown | 12 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 28 | 41% |
| Nursing and Health Professions | 5 | 7% |
| Computer Science | 4 | 6% |
| Agricultural and Biological Sciences | 3 | 4% |
| Biochemistry, Genetics and Molecular Biology | 2 | 3% |
| Other | 10 | 15% |
| Unknown | 16 | 24% |