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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial intelligence-based analysis: when PIONeeR meets QUANTIC
|
|---|---|
| Published in |
British Journal of Cancer, June 2020
|
| DOI | 10.1038/s41416-020-0918-3 |
| Pubmed ID | |
| Authors |
Joseph Ciccolini, Sébastien Benzekry, Fabrice Barlesi |
| Abstract |
This project aims to generate dense longitudinal data in lung cancer patients undergoing anti-PD1/PDL1 therapy. Mathematical modelling with mechanistic learning algorithms will help decipher the mechanisms underlying the response or resistance to immunotherapy. A better understanding of these mechanisms should help identifying actionable items to increase the efficacy of immune-checkpoint inhibitors. |
X Demographics
The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 4 | 33% |
| United Kingdom | 1 | 8% |
| Italy | 1 | 8% |
| Unknown | 6 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 75% |
| Practitioners (doctors, other healthcare professionals) | 1 | 8% |
| Science communicators (journalists, bloggers, editors) | 1 | 8% |
| Scientists | 1 | 8% |
Mendeley readers
The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 36 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Doctoral Student | 4 | 11% |
| Researcher | 4 | 11% |
| Other | 3 | 8% |
| Student > Ph. D. Student | 2 | 6% |
| Lecturer > Senior Lecturer | 1 | 3% |
| Other | 4 | 11% |
| Unknown | 18 | 50% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 6 | 17% |
| Computer Science | 3 | 8% |
| Mathematics | 2 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 3% |
| Unspecified | 1 | 3% |
| Other | 3 | 8% |
| Unknown | 20 | 56% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 04 August 2020.
All research outputs
#4,768,346
of 23,215,490 outputs
Outputs from British Journal of Cancer
#3,198
of 10,535 outputs
Outputs of similar age
#108,456
of 372,009 outputs
Outputs of similar age from British Journal of Cancer
#53
of 145 outputs
Altmetric has tracked 23,215,490 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,535 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.7. This one has gotten more attention than average, scoring higher than 69% of its peers.
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 372,009 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 145 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.