A Ligadura Tubária é Fator de Risco para a Redução da Densidade Mineral Óssea em Mulheres na Pós-menopausa?

Zabaglia, Silval Fernando Cardoso; Pinto‐Neto, Aarão Mendes

doi:10.1590/s0100-72032001001000002

Cited by 2 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A large number of risk factors have been identified witch are directly associated with an increased risk of fracture, [ 3 , 18 , 24 , 26 , 30 , 31 , 43 – 50 ], it has been noted that in some cases its suitability for inclusion in algorithms Evaluation for fracture or osteoporosis prediction was not well validated, it is also worth noting the absence of an international assessment of somatic factors [ 18 , 28 ].…”

Section: Discussionmentioning

confidence: 99%

“…Annually 8.9 million fractures occur worldwide resulting from osteoporosis, which means that every 3 s a person suffers an osteoporotic fracture. The most common sites with incidence of fractures include spine, forearm, proximal humerus and hip [ 3 ]. Hip fractures are those that present higher morbidity and mortality and generate the highest direct costs for health care services, increasing exponentially when they affect the elderly population.…”

Section: Statisticsmentioning

confidence: 99%

“…It is estimated that by 2050 the number of hip fractures in men and women aged 50–64 years in Latin America will increase by 400%. In Brazil ten million people, about one person in every seventeen, suffer from osteoporosis [ 3 ]. The aging of the population will lead to a significant increase in the occurrence of fractures, being more evident in Asia and Latin America, since there has been an increase in the incidence of osteoporotic fractures in developing countries.…”

Section: Statisticsmentioning

confidence: 99%

See 2 more Smart Citations

Artificial intelligence on the identification of risk groups for osteoporosis, a general review

et al. 2018

View full text Add to dashboard Cite

IntroductionThe goal of this paper is to present a critical review on the main systems that use artificial intelligence to identify groups at risk for osteoporosis or fractures. The systems considered for this study were those that fulfilled the following requirements: range of coverage in diagnosis, low cost and capability to identify more significant somatic factors.MethodsA bibliographic research was done in the databases, PubMed, IEEExplorer Latin American and Caribbean Center on Health Sciences Information (LILACS), Medical Literature Analysis and Retrieval System Online (MEDLINE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, Web of Science, and Science Direct searching the terms “Neural Network”, “Osteoporosis Machine Learning” and “Osteoporosis Neural Network”. Studies with titles not directly related to the research topic and older data that reported repeated strategies were excluded. The search was carried out with the descriptors in German, Spanish, French, Italian, Mandarin, Portuguese and English; but only studies written in English were found to meet the established criteria. Articles covering the period 2000–2017 were selected; however, articles prior to this period with great relevance were included in this study.DiscussionBased on the collected research, it was identified that there are several methods in the use of artificial intelligence to help the screening of risk groups of osteoporosis or fractures. However, such systems were limited to a specific ethnic group, gender or age. For future research, new challenges are presented.ConclusionsIt is necessary to develop research with the unification of different databases and grouping of the various attributes and clinical factors, in order to reach a greater comprehensiveness in the identification of risk groups of osteoporosis. For this purpose, the use of any predictive tool should be performed in different populations with greater participation of male patients and inclusion of a larger age range for the ones involved. The biggest challenge is to deal with all the data complexity generated by this unification, developing evidence-based standards for the evaluation of the most significant risk factors.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Statisticsmentioning

confidence: 99%

Section: Statisticsmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence on the identification of risk groups for osteoporosis, a general review

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Ao ser diagnosticada e tratada, essa chance é reduzida para 1 em 8 (KAPTOGE et al, 2004). No Brasil, apenas um terço dos pacientes com fratura no quadril é diagnosticado e desses apenas 1 em cada 5 pacientes recebe um tratamento (ZABAGLIA; COSTA-PAIVA; PINTO-NETO, 2001). (SVEDBOM et al, 2013).…”

Section: Introdução 11 Osteoporoseunclassified

Utilização dos fatores peptídicos de crescimento recombinantes humanos rhBMP7 (proteína morfogenética óssea 7) e rhPDGF-BB (fator de crescimento derivado de plaquetas BB) visando o tratamento da osteoporose em modelo de ratas ooforectomizadas

Reis¹

View full text Add to dashboard Cite

show abstract

A Ligadura Tubária é Fator de Risco para a Redução da Densidade Mineral Óssea em Mulheres na Pós-menopausa?

Cited by 2 publications

References 14 publications

Artificial intelligence on the identification of risk groups for osteoporosis, a general review

Artificial intelligence on the identification of risk groups for osteoporosis, a general review

Utilização dos fatores peptídicos de crescimento recombinantes humanos rhBMP7 (proteína morfogenética óssea 7) e rhPDGF-BB (fator de crescimento derivado de plaquetas BB) visando o tratamento da osteoporose em modelo de ratas ooforectomizadas

Contact Info

Product

Resources

About