| Title |
Infant EEG activity as a biomarker for autism: a promising approach or a false promise?
|
|---|---|
| Published in |
BMC Medicine, May 2011
|
| DOI | 10.1186/1741-7015-9-61 |
| Pubmed ID | |
| Authors |
Richard Griffin, Chris Westbury |
| Abstract |
The ability to determine an infant's likelihood of developing autism via a relatively simple neurological measure would constitute an important scientific breakthrough. In their recent publication in this journal, Bosl and colleagues claim that a measure of EEG complexity can be used to detect, with very high accuracy, infants at high risk for autism (HRA). On the surface, this appears to be that very scientific breakthrough and as such the paper has received widespread media attention. But a close look at how these high accuracy rates were derived tells a very different story. This stems from a conflation between "high risk" as a population-level property and "high risk" as a property of an individual. We describe the approach of Bosl et al. and examine their results with respect to baseline prevalence rates, the inclusion of which is necessary to distinguish infants with a biological risk of autism from typically developing infants with a sibling with autism. This is an important distinction that should not be overlooked. Please see research article: http://www.biomedcentral.com/1741-7015/9/18 and correspondence article: http://www.biomedcentral.com/1741-7015/9/60. |
Mendeley readers
The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 1% |
| United Kingdom | 1 | 1% |
| United States | 1 | 1% |
| Poland | 1 | 1% |
| Unknown | 80 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 18 | 21% |
| Researcher | 18 | 21% |
| Student > Master | 13 | 15% |
| Professor | 6 | 7% |
| Student > Postgraduate | 5 | 6% |
| Other | 18 | 21% |
| Unknown | 6 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 26 | 31% |
| Medicine and Dentistry | 13 | 15% |
| Agricultural and Biological Sciences | 8 | 10% |
| Neuroscience | 7 | 8% |
| Engineering | 5 | 6% |
| Other | 12 | 14% |
| Unknown | 13 | 15% |
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 10 September 2011.
All research outputs
#5,563,279
of 22,710,079 outputs
Outputs from BMC Medicine
#2,226
of 3,406 outputs
Outputs of similar age
#31,102
of 111,581 outputs
Outputs of similar age from BMC Medicine
#23
of 41 outputs
Altmetric has tracked 22,710,079 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,406 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 43.5. This one is in the 34th percentile – i.e., 34% 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 111,581 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 72% of its contemporaries.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.