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Prevalence of the Linburg-Comstock Anomaly in a Brazilian Population Sample.

Leonardo Coêlho de Alencar BarretoCarlos Henrique FernandesLuís Renato NakachimaJoão Baptista Gomes Dos SantosMarcela FernandesFlavio Faloppa
Published in: Revista brasileira de ortopedia (2020)
Objective  To determine the prevalence of the Linburg-Comstock anomaly in a Brazilian population sample. Methods  A cross-sectional observational study was carried out between October 2017 and April 2018. We included male and female volunteers aged 18 years or older. The presence of the Linburg-Comstock anomaly was determined by performing the clinical tests described by Linburg and Comstock. The data were analyzed using the GraphPad Prism software, and we considered differences with p  < 0.05. Results  The study analyzed 1,008 volunteers (2,016 hands) with a mean age of 38.3 years, 531 (52.67%) of which were male, and 477 (47.33%) were female. The Linburg-Comstock anomaly was diagnosed in 564 (55.95%) individuals, and it was bilateral in 300 (53.2%) of them, right-sided in 162 (28.72%), and left-sided in 102 (18.08%). No significant differences were found when comparing the prevalence between genders. However, a the prevalence of the right-sided anomaly in the male population (n = 99; 70.21%) was higher than in the female one (n = 63; 51.21%), with p  = 0.0016. In addition, the presence of pain by the maneuver described by Linburg and Comstock was more prevalent in women (n = 150; 54.94%) than in men (n = 105; 36.08%), with p  = 0.0001. These results show the importance of epidemiological studies on the Linburg-Comstock anomaly, mainly in order to investigate the presence of associated conditions. Conclusion  The prevalence of the Linburg-Comstock anomaly in the studied population was of 55.95%, and it was bilateral in 53.2% of the volunteers. The presence of the connection was observed more frequently in the right side and among men, but the pain symptom was more frequent among women.
Keyphrases
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  • chronic pain
  • polycystic ovary syndrome
  • machine learning
  • physical activity
  • pregnant women
  • spinal cord
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  • insulin resistance
  • big data
  • community dwelling