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Mendeley readers
Attention Score in Context
| Title |
An inflammation-related nomogram for predicting the survival of patients with non-small cell lung cancer after pulmonary lobectomy
|
|---|---|
| Published in |
BMC Cancer, June 2018
|
| DOI | 10.1186/s12885-018-4513-4 |
| Pubmed ID | |
| Authors |
Ying Wang, Xiao Qu, Ngar-Woon Kam, Kai Wang, Hongchang Shen, Qi Liu, Jiajun Du |
| Abstract |
Emerging inflammatory response biomarkers are developed to predict the survival of patients with cancer, the aim of our study is to establish an inflammation-related nomogram based on the classical predictive biomarkers to predict the survivals of patients with non-small cell lung cancer (NSCLC). Nine hundred and fifty-two NSCLC patients with lung cancer surgery performed were enrolled into this study. The cutoffs of inflammatory response biomarkers were determined by Receiver operating curve (ROC). Univariate and multivariate analysis were conducted to select independent prognostic factors to develop the nomogram. The median follow-up time was 40.0 months (range, 1 to 92 months). The neutrophil to lymphocyte ratio (cut-off: 3.10, HR:1.648, P = 0.045) was selected to establish the nomogram which could predict the 5-year OS probability. The C-index of nomogram was 0.72 and the 5-year OS calibration curve displayed an optimal agreement between the actual observed outcomes and the predictive results. Neutrophil to lymphocyte ratio was shown to be a valuable biomarker for predicting survival of patients with NSCLC. The addition of neutrophil to lymphocyte ratio could improve the accuracy and predictability of the nomogram in order to provide reference for clinicians to assess patient outcomes. |
Mendeley readers
The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 11 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 2 | 18% |
| Student > Bachelor | 2 | 18% |
| Researcher | 1 | 9% |
| Student > Postgraduate | 1 | 9% |
| Unknown | 5 | 45% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 5 | 45% |
| Biochemistry, Genetics and Molecular Biology | 1 | 9% |
| Unknown | 5 | 45% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 July 2018.
All research outputs
#20,523,725
of 23,094,276 outputs
Outputs from BMC Cancer
#6,548
of 8,383 outputs
Outputs of similar age
#288,474
of 329,076 outputs
Outputs of similar age from BMC Cancer
#107
of 140 outputs
Altmetric has tracked 23,094,276 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,383 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 329,076 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 140 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.