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The Ottawa SAH search algorithms: protocol for a multi- centre validation study of primary subarachnoid hemorrhage prediction models using health administrative data (the SAHepi prediction study…

Overview of attention for article published in BMC Medical Research Methodology, September 2018
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  • Good Attention Score compared to outputs of the same age (68th percentile)

Mentioned by

blogs
1 blog

Citations

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3 Dimensions

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41 Mendeley
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Title
The Ottawa SAH search algorithms: protocol for a multi- centre validation study of primary subarachnoid hemorrhage prediction models using health administrative data (the SAHepi prediction study protocol)
Published in
BMC Medical Research Methodology, September 2018
DOI 10.1186/s12874-018-0553-3
Pubmed ID
Authors

S. W. English, L. McIntyre, V. Saigle, M. Chassé, D. A. Fergusson, A. F. Turgeon, F. Lauzier, D. Griesdale, A. Garland, R. Zarychanski, A. Algird, C. van Walraven

Abstract

Conducting prospective epidemiological studies of hospitalized patients with rare diseases like primary subarachnoid hemorrhage (pSAH) are difficult due to time and budgetary constraints. Routinely collected administrative data could remove these barriers. We derived and validated 3 algorithms to identify hospitalized patients with a high probability of pSAH using administrative data. We aim to externally validate their performance in four hospitals across Canada. Eligible patients include those ≥18 years of age admitted to these centres from January 1, 2012 to December 31, 2013. We will include patients whose discharge abstracts contain predictive variables identified in the models (ICD-10-CA diagnostic codes I60** (subarachnoid hemorrhage), I61** (intracranial hemorrhage), 162** (other nontrauma intracranial hemorrhage), I67** (other cerebrovascular disease), S06** (intracranial injury), G97 (other postprocedural nervous system disorder) and CCI procedural codes 1JW51 (occlusion of intracranial vessels), 1JE51 (carotid artery inclusion), 3JW10 (intracranial vessel imaging), 3FY20 (CT scan (soft tissue of neck)), and 3OT20 (CT scan (abdominal cavity)). The algorithms will be applied to each patient and the diagnosis confirmed via chart review. We will assess each model's sensitivity, specificity, negative and positive predictive value across the sites. Validating the Ottawa SAH Prediction Algorithms will provide a way to accurately identify large SAH cohorts, thereby furthering research and altering care.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 7 17%
Student > Master 4 10%
Other 3 7%
Student > Ph. D. Student 3 7%
Student > Doctoral Student 1 2%
Other 5 12%
Unknown 18 44%
Readers by discipline Count As %
Medicine and Dentistry 16 39%
Biochemistry, Genetics and Molecular Biology 1 2%
Nursing and Health Professions 1 2%
Veterinary Science and Veterinary Medicine 1 2%
Psychology 1 2%
Other 3 7%
Unknown 18 44%
Attention Score in Context

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 16 September 2018.
All research outputs
#5,832,866
of 23,103,436 outputs
Outputs from BMC Medical Research Methodology
#831
of 2,035 outputs
Outputs of similar age
#102,054
of 337,900 outputs
Outputs of similar age from BMC Medical Research Methodology
#19
of 36 outputs
Altmetric has tracked 23,103,436 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 2,035 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has gotten more attention than average, scoring higher than 55% 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 337,900 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 68% of its contemporaries.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.