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Mendeley readers
Attention Score in Context
| Title |
Clinical Prediction Models for Aneurysmal Subarachnoid Hemorrhage: A Systematic Review
|
|---|---|
| Published in |
Neurocritical Care, November 2012
|
| DOI | 10.1007/s12028-012-9792-z |
| Pubmed ID | |
| Authors |
Blessing N. R. Jaja, Michael D. Cusimano, Nima Etminan, Daniel Hanggi, David Hasan, Don Ilodigwe, Hector Lantigua, Peter Le Roux, Benjamin Lo, Ada Louffat-Olivares, Stephan Mayer, Andrew Molyneux, Audrey Quinn, Tom A. Schweizer, Thomas Schenk, Julian Spears, Michael Todd, James Torner, Mervyn D. I. Vergouwen, George K. C. Wong, Jeff Singh, R. Loch Macdonald |
| Abstract |
Clinical prediction models can enhance clinical decision-making and research. However, available prediction models in aneurysmal subarachnoid hemorrhage (aSAH) are rarely used. We evaluated the methodological validity of SAH prediction models and the relevance of the main predictors to identify potentially reliable models and to guide future attempts at model development. |
Mendeley readers
The data shown below were compiled from readership statistics for 128 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | <1% |
| Denmark | 1 | <1% |
| Canada | 1 | <1% |
| Unknown | 125 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 28 | 22% |
| Student > Ph. D. Student | 13 | 10% |
| Student > Master | 13 | 10% |
| Student > Doctoral Student | 11 | 9% |
| Student > Postgraduate | 8 | 6% |
| Other | 28 | 22% |
| Unknown | 27 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 58 | 45% |
| Neuroscience | 15 | 12% |
| Computer Science | 4 | 3% |
| Agricultural and Biological Sciences | 3 | 2% |
| Nursing and Health Professions | 2 | 2% |
| Other | 8 | 6% |
| Unknown | 38 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 09 June 2019.
All research outputs
#4,671,146
of 22,685,926 outputs
Outputs from Neurocritical Care
#437
of 1,491 outputs
Outputs of similar age
#35,563
of 182,177 outputs
Outputs of similar age from Neurocritical Care
#3
of 13 outputs
Altmetric has tracked 22,685,926 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,491 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.4. 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 182,177 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.