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Showing 1 to 12 of 27 entries
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General practitioner management of genetic aspects of a cardiac disease: a scenario-based study to anticipate providers' practices.

Journal of community genetics

Challen K, Harris H, Kristoffersson U, Nippert I, Schmidtke J, Ten Kate LP, Benjamin C, Anionwu E, Plass AM, Julian-Reynier C, Harris R.
PMID: 21475668
J Community Genet. 2010 Jun;1(2):83-90. doi: 10.1007/s12687-010-0013-1. Epub 2010 Aug 09.

It is increasingly recognised that genetics will have to be integrated into all parts of primary health care. Previous research has demonstrated that involvement and confidence in genetics varies amongst primary care providers. We aimed to analyse perceptions of...

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Challen K, Harris H, Kristoffersson U, et al. General practitioner management of genetic aspects of a cardiac disease: a scenario-based study to anticipate providers' practices. J Community Genet. 2010;1(2):83-90doi: 10.1007/s12687-010-0013-1.
Challen, K., Harris, H., Kristoffersson, U., Nippert, I., Schmidtke, J., Ten Kate, L. P., Benjamin, C., Anionwu, E., Plass, A. M., Julian-Reynier, C., & Harris, R. (2010). General practitioner management of genetic aspects of a cardiac disease: a scenario-based study to anticipate providers' practices. Journal of community genetics, 1(2), 83-90. https://doi.org/10.1007/s12687-010-0013-1
Challen, Kirsty, et al. "General practitioner management of genetic aspects of a cardiac disease: a scenario-based study to anticipate providers' practices." Journal of community genetics vol. 1,2 (2010): 83-90. doi: https://doi.org/10.1007/s12687-010-0013-1
Challen K, Harris H, Kristoffersson U, Nippert I, Schmidtke J, Ten Kate LP, Benjamin C, Anionwu E, Plass AM, Julian-Reynier C, Harris R. General practitioner management of genetic aspects of a cardiac disease: a scenario-based study to anticipate providers' practices. J Community Genet. 2010 Jun;1(2):83-90. doi: 10.1007/s12687-010-0013-1. Epub 2010 Aug 09. PMID: 21475668; PMCID: PMC3063843.
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Anatomical basis for surgical approaches to the hip.

Annals of medical and health sciences research

Onyemaechi N, Anyanwu E, Obikili E, Ekezie J.
PMID: 25221692
Ann Med Health Sci Res. 2014 Jul;4(4):487-94. doi: 10.4103/2141-9248.139278.

The hip joint is one of the most surgically exposed joints in the body. The indications for surgical exposure are numerous ranging from simple procedures such as arthrotomy for joint drainage in infection to complex procedures like revised total...

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Onyemaechi N, Anyanwu E, Obikili E, et al. Anatomical basis for surgical approaches to the hip. Ann Med Health Sci Res. 2014;4(4):487-94doi: 10.4103/2141-9248.139278.
Onyemaechi, N., Anyanwu, E., Obikili, E., & Ekezie, J. (2014). Anatomical basis for surgical approaches to the hip. Annals of medical and health sciences research, 4(4), 487-94. https://doi.org/10.4103/2141-9248.139278
Onyemaechi, Noc, et al. "Anatomical basis for surgical approaches to the hip." Annals of medical and health sciences research vol. 4,4 (2014): 487-94. doi: https://doi.org/10.4103/2141-9248.139278
Onyemaechi N, Anyanwu E, Obikili E, Ekezie J. Anatomical basis for surgical approaches to the hip. Ann Med Health Sci Res. 2014 Jul;4(4):487-94. doi: 10.4103/2141-9248.139278. PMID: 25221692; PMCID: PMC4160668.
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VOICES-Sexually sidelined Elizabeth Anionwu says that fresh air and spring flowers lift her spirits.

Nursing standard (Royal College of Nursing (Great Britain) : 1987)

Anionwu E.
PMID: 28086584
Nurs Stand. 2011 Feb 02;25(22):26. doi: 10.7748/ns.25.22.26.s29.

I have never understood why my mood seems to lift once January is over. Is it because I have submitted my online tax return by the dreaded deadline? Or maybe it is hope that the weather will start to...

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Anionwu E. VOICES-Sexually sidelined Elizabeth Anionwu says that fresh air and spring flowers lift her spirits. Nurs Stand. 2011;25(22):26doi: 10.7748/ns.25.22.26.s29.
Anionwu, E. (2011). VOICES-Sexually sidelined Elizabeth Anionwu says that fresh air and spring flowers lift her spirits. Nursing standard (Royal College of Nursing (Great Britain) : 1987), 25(22), 26. https://doi.org/10.7748/ns.25.22.26.s29
Anionwu, Elizabeth. "VOICES-Sexually sidelined Elizabeth Anionwu says that fresh air and spring flowers lift her spirits." Nursing standard (Royal College of Nursing (Great Britain) : 1987) vol. 25,22 (2011): 26. doi: https://doi.org/10.7748/ns.25.22.26.s29
Anionwu E. VOICES-Sexually sidelined Elizabeth Anionwu says that fresh air and spring flowers lift her spirits. Nurs Stand. 2011 Feb 02;25(22):26. doi: 10.7748/ns.25.22.26.s29. PMID: 28086584.
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Voices - Sickle cell disease has some little-known complications, says Elizabeth Anionwu.

Nursing standard (Royal College of Nursing (Great Britain) : 1987)

Anionwu E.
PMID: 28071573
Nurs Stand. 2011 Jun 08;25(40):26. doi: 10.7748/ns.25.40.26.s27.

Recently I attended an event aimed at raising awareness about the increased risk of stroke among people with sickle cell disease.

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Anionwu E. Voices - Sickle cell disease has some little-known complications, says Elizabeth Anionwu. Nurs Stand. 2011;25(40):26doi: 10.7748/ns.25.40.26.s27.
Anionwu, E. (2011). Voices - Sickle cell disease has some little-known complications, says Elizabeth Anionwu. Nursing standard (Royal College of Nursing (Great Britain) : 1987), 25(40), 26. https://doi.org/10.7748/ns.25.40.26.s27
Anionwu, Elizabeth. "Voices - Sickle cell disease has some little-known complications, says Elizabeth Anionwu." Nursing standard (Royal College of Nursing (Great Britain) : 1987) vol. 25,40 (2011): 26. doi: https://doi.org/10.7748/ns.25.40.26.s27
Anionwu E. Voices - Sickle cell disease has some little-known complications, says Elizabeth Anionwu. Nurs Stand. 2011 Jun 08;25(40):26. doi: 10.7748/ns.25.40.26.s27. PMID: 28071573.
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Comparison of machine learning and deep learning for view identification from cardiac magnetic resonance images.

Clinical imaging

Chauhan D, Anyanwu E, Goes J, Besser SA, Anand S, Madduri R, Getty N, Kelle S, Kawaji K, Mor-Avi V, Patel AR.
PMID: 34813989
Clin Imaging. 2021 Nov 19;82:121-126. doi: 10.1016/j.clinimag.2021.11.013. Epub 2021 Nov 19.

BACKGROUND: Artificial intelligence is increasingly utilized to aid in the interpretation of cardiac magnetic resonance (CMR) studies. One of the first steps is the identification of the imaging plane depicted, which can be achieved by both deep learning (DL)...

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Chauhan D, Anyanwu E, Goes J, et al. Comparison of machine learning and deep learning for view identification from cardiac magnetic resonance images. Clin Imaging. 2021;82:121-126doi: 10.1016/j.clinimag.2021.11.013.
Chauhan, D., Anyanwu, E., Goes, J., Besser, S. A., Anand, S., Madduri, R., Getty, N., Kelle, S., Kawaji, K., Mor-Avi, V., & Patel, A. R. (2021). Comparison of machine learning and deep learning for view identification from cardiac magnetic resonance images. Clinical imaging, 82121-126. https://doi.org/10.1016/j.clinimag.2021.11.013
Chauhan, Daksh, et al. "Comparison of machine learning and deep learning for view identification from cardiac magnetic resonance images." Clinical imaging vol. 82 (2021): 121-126. doi: https://doi.org/10.1016/j.clinimag.2021.11.013
Chauhan D, Anyanwu E, Goes J, Besser SA, Anand S, Madduri R, Getty N, Kelle S, Kawaji K, Mor-Avi V, Patel AR. Comparison of machine learning and deep learning for view identification from cardiac magnetic resonance images. Clin Imaging. 2021 Nov 19;82:121-126. doi: 10.1016/j.clinimag.2021.11.013. Epub 2021 Nov 19. PMID: 34813989.
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Comparison of machine learning and deep learning for view identification from cardiac magnetic resonance images.

Clinical imaging

Chauhan D, Anyanwu E, Goes J, Besser SA, Anand S, Madduri R, Getty N, Kelle S, Kawaji K, Mor-Avi V, Patel AR.
PMID: 34813989
Clin Imaging. 2021 Nov 19;82:121-126. doi: 10.1016/j.clinimag.2021.11.013. Epub 2021 Nov 19.

BACKGROUND: Artificial intelligence is increasingly utilized to aid in the interpretation of cardiac magnetic resonance (CMR) studies. One of the first steps is the identification of the imaging plane depicted, which can be achieved by both deep learning (DL)...

Cite
Chauhan D, Anyanwu E, Goes J, et al. Comparison of machine learning and deep learning for view identification from cardiac magnetic resonance images. Clin Imaging. 2021;82:121-126doi: 10.1016/j.clinimag.2021.11.013.
Chauhan, D., Anyanwu, E., Goes, J., Besser, S. A., Anand, S., Madduri, R., Getty, N., Kelle, S., Kawaji, K., Mor-Avi, V., & Patel, A. R. (2021). Comparison of machine learning and deep learning for view identification from cardiac magnetic resonance images. Clinical imaging, 82121-126. https://doi.org/10.1016/j.clinimag.2021.11.013
Chauhan, Daksh, et al. "Comparison of machine learning and deep learning for view identification from cardiac magnetic resonance images." Clinical imaging vol. 82 (2021): 121-126. doi: https://doi.org/10.1016/j.clinimag.2021.11.013
Chauhan D, Anyanwu E, Goes J, Besser SA, Anand S, Madduri R, Getty N, Kelle S, Kawaji K, Mor-Avi V, Patel AR. Comparison of machine learning and deep learning for view identification from cardiac magnetic resonance images. Clin Imaging. 2021 Nov 19;82:121-126. doi: 10.1016/j.clinimag.2021.11.013. Epub 2021 Nov 19. PMID: 34813989.
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Strategies to promote bme staff are not being enforced.

Nursing standard (Royal College of Nursing (Great Britain) : 1987)

Anionwu E.
PMID: 28080594
Nurs Stand. 2009 Jan 21;23(20):33. doi: 10.7748/ns.23.20.33.s47.

The NHS Institute for Innovation and Improvement report Access of BME Staff to Senior Positions in the NHS (news January 14) put a bit of damper on my new year.

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Anionwu E. Strategies to promote bme staff are not being enforced. Nurs Stand. 2009;23(20):33doi: 10.7748/ns.23.20.33.s47.
Anionwu, E. (2009). Strategies to promote bme staff are not being enforced. Nursing standard (Royal College of Nursing (Great Britain) : 1987), 23(20), 33. https://doi.org/10.7748/ns.23.20.33.s47
Anionwu, Elizabeth. "Strategies to promote bme staff are not being enforced." Nursing standard (Royal College of Nursing (Great Britain) : 1987) vol. 23,20 (2009): 33. doi: https://doi.org/10.7748/ns.23.20.33.s47
Anionwu E. Strategies to promote bme staff are not being enforced. Nurs Stand. 2009 Jan 21;23(20):33. doi: 10.7748/ns.23.20.33.s47. PMID: 28080594.
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Appeal ambassadors ensuring the Seacole legacy survives.

Nursing standard (Royal College of Nursing (Great Britain) : 1987)

Anionwu E.
PMID: 26463802
Nurs Stand. 2015 Oct 14;30(7):31. doi: 10.7748/ns.30.7.31.s39.

No abstract available.

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Anionwu E. Appeal ambassadors ensuring the Seacole legacy survives. Nurs Stand. 2015;30(7):31doi: 10.7748/ns.30.7.31.s39.
Anionwu, E. (2015). Appeal ambassadors ensuring the Seacole legacy survives. Nursing standard (Royal College of Nursing (Great Britain) : 1987), 30(7), 31. https://doi.org/10.7748/ns.30.7.31.s39
Anionwu, Elizabeth. "Appeal ambassadors ensuring the Seacole legacy survives." Nursing standard (Royal College of Nursing (Great Britain) : 1987) vol. 30,7 (2015): 31. doi: https://doi.org/10.7748/ns.30.7.31.s39
Anionwu E. Appeal ambassadors ensuring the Seacole legacy survives. Nurs Stand. 2015 Oct 14;30(7):31. doi: 10.7748/ns.30.7.31.s39. PMID: 26463802.
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Comparison of machine learning and deep learning for view identification from cardiac magnetic resonance images.

Clinical imaging

Chauhan D, Anyanwu E, Goes J, Besser SA, Anand S, Madduri R, Getty N, Kelle S, Kawaji K, Mor-Avi V, Patel AR.
PMID: 34813989
Clin Imaging. 2021 Nov 19;82:121-126. doi: 10.1016/j.clinimag.2021.11.013. Epub 2021 Nov 19.

BACKGROUND: Artificial intelligence is increasingly utilized to aid in the interpretation of cardiac magnetic resonance (CMR) studies. One of the first steps is the identification of the imaging plane depicted, which can be achieved by both deep learning (DL)...

Cite
Chauhan D, Anyanwu E, Goes J, et al. Comparison of machine learning and deep learning for view identification from cardiac magnetic resonance images. Clin Imaging. 2021;82:121-126doi: 10.1016/j.clinimag.2021.11.013.
Chauhan, D., Anyanwu, E., Goes, J., Besser, S. A., Anand, S., Madduri, R., Getty, N., Kelle, S., Kawaji, K., Mor-Avi, V., & Patel, A. R. (2021). Comparison of machine learning and deep learning for view identification from cardiac magnetic resonance images. Clinical imaging, 82121-126. https://doi.org/10.1016/j.clinimag.2021.11.013
Chauhan, Daksh, et al. "Comparison of machine learning and deep learning for view identification from cardiac magnetic resonance images." Clinical imaging vol. 82 (2021): 121-126. doi: https://doi.org/10.1016/j.clinimag.2021.11.013
Chauhan D, Anyanwu E, Goes J, Besser SA, Anand S, Madduri R, Getty N, Kelle S, Kawaji K, Mor-Avi V, Patel AR. Comparison of machine learning and deep learning for view identification from cardiac magnetic resonance images. Clin Imaging. 2021 Nov 19;82:121-126. doi: 10.1016/j.clinimag.2021.11.013. Epub 2021 Nov 19. PMID: 34813989.
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Neurobehavioural and neurophysiological effects of marijuana use among the adolescents in Birmingham, UK.

International journal of adolescent medicine and health

Anyanwu E.
PMID: 22912287
Int J Adolesc Med Health. 2011 May 20;12(2):141-58. doi: 10.1515/IJAMH.2000.12.2-3.141.

No abstract available.

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Anyanwu E. Neurobehavioural and neurophysiological effects of marijuana use among the adolescents in Birmingham, UK. Int J Adolesc Med Health. 2011;12(2):141-58doi: 10.1515/IJAMH.2000.12.2-3.141.
Anyanwu, E. (2011). Neurobehavioural and neurophysiological effects of marijuana use among the adolescents in Birmingham, UK. International journal of adolescent medicine and health, 12(2), 141-58. https://doi.org/10.1515/IJAMH.2000.12.2-3.141
Anyanwu, Ebere. "Neurobehavioural and neurophysiological effects of marijuana use among the adolescents in Birmingham, UK." International journal of adolescent medicine and health vol. 12,2 (2011): 141-58. doi: https://doi.org/10.1515/IJAMH.2000.12.2-3.141
Anyanwu E. Neurobehavioural and neurophysiological effects of marijuana use among the adolescents in Birmingham, UK. Int J Adolesc Med Health. 2011 May 20;12(2):141-58. doi: 10.1515/IJAMH.2000.12.2-3.141. PMID: 22912287.
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Making the most of retirement.

Nursing standard (Royal College of Nursing (Great Britain) : 1987)

Bennett T, Anionwu E.
PMID: 18087883
Nurs Stand. 2007 Nov 28-Dec 4;22(12):64.

No abstract available.

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Bennett T, Anionwu E. Making the most of retirement. Nurs Stand. 2007;22(12):64
Bennett, T., & Anionwu, E. (2007). Making the most of retirement. Nursing standard (Royal College of Nursing (Great Britain) : 1987), 22(12), 64.
Bennett, Teresa, and Anionwu, Elizabeth. "Making the most of retirement." Nursing standard (Royal College of Nursing (Great Britain) : 1987) vol. 22,12 (2007): 64.
Bennett T, Anionwu E. Making the most of retirement. Nurs Stand. 2007 Nov 28-Dec 4;22(12):64. PMID: 18087883.
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Fair provision.

Nursing standard (Royal College of Nursing (Great Britain) : 1987)

Anionwu E.
PMID: 27684696
Nurs Stand. 1995 Dec 13;10(14):45. doi: 10.7748/ns.10.14.45.s42.

With winter's arrival my thoughts turn to those affected by sickle cell disorders. One of the most common problems for people suffering from this inherited blood disorder is the painful 'crisis' that accounts for over 90 per cent of...

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Anionwu E. Fair provision. Nurs Stand. 1995;10(14):45doi: 10.7748/ns.10.14.45.s42.
Anionwu, E. (1995). Fair provision. Nursing standard (Royal College of Nursing (Great Britain) : 1987), 10(14), 45. https://doi.org/10.7748/ns.10.14.45.s42
Anionwu, Elizabeth. "Fair provision." Nursing standard (Royal College of Nursing (Great Britain) : 1987) vol. 10,14 (1995): 45. doi: https://doi.org/10.7748/ns.10.14.45.s42
Anionwu E. Fair provision. Nurs Stand. 1995 Dec 13;10(14):45. doi: 10.7748/ns.10.14.45.s42. PMID: 27684696.
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Showing 1 to 12 of 27 entries