Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 1 to 2 of 2 entries
Sorted by: Best Match Show Resources per page
The Stanford Hall consensus statement for post-COVID-19 rehabilitation.

British journal of sports medicine

Barker-Davies RM, O'Sullivan O, Senaratne KPP, Baker P, Cranley M, Dharm-Datta S, Ellis H, Goodall D, Gough M, Lewis S, Norman J, Papadopoulou T, Roscoe D, Sherwood D, Turner P, Walker T, Mistlin A, Phillip R, Nicol AM, Bennett AN, Bahadur S.
PMID: 32475821
Br J Sports Med. 2020 Aug;54(16):949-959. doi: 10.1136/bjsports-2020-102596. Epub 2020 May 31.

The highly infectious and pathogenic novel coronavirus (CoV), severe acute respiratory syndrome (SARS)-CoV-2, has emerged causing a global pandemic. Although COVID-19 predominantly affects the respiratory system, evidence indicates a multisystem disease which is frequently severe and often results in...

Cite
Barker-Davies RM, O'Sullivan O, Senaratne KPP, et al. The Stanford Hall consensus statement for post-COVID-19 rehabilitation. Br J Sports Med. 2020;54(16):949-959doi: 10.1136/bjsports-2020-102596.
Barker-Davies, R. M., O'Sullivan, O., Senaratne, K. P. P., Baker, P., Cranley, M., Dharm-Datta, S., Ellis, H., Goodall, D., Gough, M., Lewis, S., Norman, J., Papadopoulou, T., Roscoe, D., Sherwood, D., Turner, P., Walker, T., Mistlin, A., Phillip, R., Nicol, A. M., Bennett, A. N., & Bahadur, S. (2020). The Stanford Hall consensus statement for post-COVID-19 rehabilitation. British journal of sports medicine, 54(16), 949-959. https://doi.org/10.1136/bjsports-2020-102596
Barker-Davies, Robert M, et al. "The Stanford Hall consensus statement for post-COVID-19 rehabilitation." British journal of sports medicine vol. 54,16 (2020): 949-959. doi: https://doi.org/10.1136/bjsports-2020-102596
Barker-Davies RM, O'Sullivan O, Senaratne KPP, Baker P, Cranley M, Dharm-Datta S, Ellis H, Goodall D, Gough M, Lewis S, Norman J, Papadopoulou T, Roscoe D, Sherwood D, Turner P, Walker T, Mistlin A, Phillip R, Nicol AM, Bennett AN, Bahadur S. The Stanford Hall consensus statement for post-COVID-19 rehabilitation. Br J Sports Med. 2020 Aug;54(16):949-959. doi: 10.1136/bjsports-2020-102596. Epub 2020 May 31. PMID: 32475821; PMCID: PMC7418628.
Copy Download .nbib Format: NLM
Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study.

JMIR public health and surveillance

Mackey T, Purushothaman V, Li J, Shah N, Nali M, Bardier C, Liang B, Cai M, Cuomo R.
PMID: 32490846
JMIR Public Health Surveill. 2020 Jun 08;6(2):e19509. doi: 10.2196/19509.

BACKGROUND: The coronavirus disease (COVID-19) pandemic is a global health emergency with over 6 million cases worldwide as of the beginning of June 2020. The pandemic is historic in scope and precedent given its emergence in an increasingly digital...

Cite
Mackey T, Purushothaman V, Li J, et al. Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study. JMIR Public Health Surveill. 2020;6(2):e19509doi: 10.2196/19509.
Mackey, T., Purushothaman, V., Li, J., Shah, N., Nali, M., Bardier, C., Liang, B., Cai, M., & Cuomo, R. (2020). Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study. JMIR public health and surveillance, 6(2), e19509. https://doi.org/10.2196/19509
Mackey, Tim, et al. "Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study." JMIR public health and surveillance vol. 6,2 (2020): e19509. doi: https://doi.org/10.2196/19509
Mackey T, Purushothaman V, Li J, Shah N, Nali M, Bardier C, Liang B, Cai M, Cuomo R. Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study. JMIR Public Health Surveill. 2020 Jun 08;6(2):e19509. doi: 10.2196/19509. PMID: 32490846; PMCID: PMC7282475.
Copy Download .nbib Format: NLM
Showing 1 to 2 of 2 entries