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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Automated comparison of last hospital main diagnosis and underlying cause of death ICD10 codes, France, 2008–2009
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, June 2014
|
| DOI | 10.1186/1472-6947-14-44 |
| Pubmed ID | |
| Authors |
Agathe Lamarche-Vadel, Gérard Pavillon, Albertine Aouba, Lars Age Johansson, Laurence Meyer, Eric Jougla, Grégoire Rey |
| Abstract |
In the age of big data in healthcare, automated comparison of medical diagnoses in large scale databases is a key issue. Our objectives were: 1) to formally define and identify cases of independence between last hospitalization main diagnosis (MD) and death registry underlying cause of death (UCD) for deceased subjects hospitalized in their last year of life; 2) to study their distribution according to socio-demographic and medico-administrative variables; 3) to discuss the interest of this method in the specific context of hospital quality of care assessment. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 50% |
| United Kingdom | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 2% |
| Unknown | 58 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 20% |
| Student > Bachelor | 8 | 14% |
| Student > Master | 6 | 10% |
| Student > Ph. D. Student | 6 | 10% |
| Student > Doctoral Student | 3 | 5% |
| Other | 8 | 14% |
| Unknown | 16 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 11 | 19% |
| Business, Management and Accounting | 4 | 7% |
| Psychology | 4 | 7% |
| Pharmacology, Toxicology and Pharmaceutical Science | 4 | 7% |
| Nursing and Health Professions | 3 | 5% |
| Other | 12 | 20% |
| Unknown | 21 | 36% |
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 12 June 2014.
All research outputs
#17,722,094
of 22,757,090 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,498
of 1,985 outputs
Outputs of similar age
#155,720
of 228,027 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#18
of 23 outputs
Altmetric has tracked 22,757,090 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,985 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 21st percentile – i.e., 21% 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 228,027 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.