1) La descarga del recurso depende de la página de origen
2) Para poder descargar el recurso, es necesario ser usuario registrado en Universia


Opción 1: Descargar recurso

Detalles del recurso

Descripción

Background: Overdiagnosis is commonly defined as a diagnosis of "disease" which will never cause symptoms or death during a patient's lifetime. Similarly, overdiagnosis can also happen when individuals are given the diagnosis of being at risk for a disease, such as being at high-risk for developing breast cancer. Women can be given such a diagnosis by meeting a set of risk assessment criteria, which are often accompanied by recommended management strategies. We sought to identify the extent and consequences of overdiagnosis for individuals being at high risk for breast cancer using the American Cancer Society (ACS) guidelines for the appropriate use of Magnetic Resonance Imaging (MRI). Methods: We identified women who fit the ACS criteria in a population based sample at a community hospital. The ACS criteria mentions three risk assessment models for determining a woman’s risk, and these criteria were reviewed to determine the extent of possible overdiagnosis in this population. The expected resource utilization resulting from this overdiagnosis, and the impact on patient quality of life are extrapolated. Results: 5,894 women who received mammography screening at the study site were included. 342 (5.8%) of the women were diagnosed as high risk by at least one model. However, only 0.2% of the total study population were diagnosed as high risk by all three models. One model identified 330 (5.6%) to be at high risk, while the other two models identified many fewer eligible women (25, 0.4% and 54, 0.9% respectively). Conclusions: Using different models to evaluation the ACS criteria identifies very different populations, implying a large potential for overdiagnosis. Further, this overdiagnosis is likely to result in the outcome of screening too many women, incurring false positives and unnecessary resource utilization.

Pertenece a

Digital Access to Scholarship at Harvard  

Autor(es)

Ozanne, Elissa -  Drohan, Brian -  Hughes, Kevin S. - 

Id.: 71304141

Idioma: inglés (Estados Unidos)  - 

Versión: 1.0

Estado: Final

Tipo de recurso: Journal Article  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

: open

Requerimientos técnicos:  Browser: Any - 

Relación: [References] doi:10.1200/jco.2013.31.31_suppl.184
[References] Journal of Clinical Oncology

Fecha de contribución: 13-may-2018

Contacto:

Localización:
* Ozanne, Elissa, Brian Drohan, and Kevin S. Hughes. 2013. “Breast Cancer Risk Assessment: How Risk Models Can ‘overdiagnose’ Risk.” Journal of Clinical Oncology 31 (31_suppl) (November): 184–184. doi:10.1200/jco.2013.31.31_suppl.184.
* 0732-183X

Otros recursos de la mismacolección

  1. Minimizing Bias in Systematic Reviews: Comment on Kunisaki et al, Provider types and Outcomes in Obstructive Sleep Apnea Case Finding and Treatment: A Systematic Review
  2. A Requiem for the Clinical Use of the Epworth Sleepiness Scale
  3. A Novel Artificial Neural Network Based Sleep-Disordered Breathing Screening Tool Study Objectives: This study evaluated a novel artificial neural network (ANN) based sleep disordere...
  4. The latent human herpesvirus-6A genome specifically integrates in telomeres of human chromosomes in vivo and in vitro Previous research has suggested that human herpesvirus-6 (HHV-6) may integrate into host cell chromo...
  5. A Comparison of Pregnancies That Occur Before and After the Onset of Chronic Fatigue Syndrome Background Many women with chronic fatigue syndrome (CFS) fear that pregnancy will worsen their con...

Aviso de cookies: Usamos cookies propias y de terceros para mejorar nuestros servicios, para análisis estadístico y para mostrarle publicidad. Si continua navegando consideramos que acepta su uso en los términos establecidos en la Política de cookies.