FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4383313236> ?p ?o ?g. }

• W4383313236 abstract "Abstract Importance Multisite pain is a major adverse health outcome in the adolescent population, affecting the daily lives of up to every third adolescent and their families. Objective To 1) predict multisite pain incidence in the whole body and in the musculoskeletal locations in adolescents, and 2) explore the sex-specific predictors of multisite pain incidence with a novel machine learning approach. Design A 2-year observational study (2013-2015). Three different baseline data sets were utilized to predict multisite pain incidence during the follow-up. Setting Population-based sample of Finnish adolescents. Participants Apparently healthy adolescents. Exposures The first data set included 48 selected baseline variables relevant for adolescents’ health and wellbeing. Data included information on students self-reported, objectively measured, and device-based demographics, physical and psychosocial characteristics, and lifestyle factors. The second data set included nine physical fitness variables related to the Finnish national ‘Move!’ monitoring and surveillance system for health-related fitness. The third data set included all available baseline data (392 variables). Main Outcome and Measures Onset of multisite pain (=weekly pain during the past three months manifesting in at least three sites and not related to any known disease or injury) during the 2-year follow-up in the whole body or musculoskeletal locations. Musculoskeletal pain sites included the neck/shoulder, upper extremities, chest, upper back, low back, buttocks, and lower extremities. Whole body pain sites also the head and abdominal areas. A machine learning algorithm random forest was utilized. Results Among 410 participants (57% girls) aged on average 12.5-years (SD 1.2), 16 % of boys and 28 % of girls developed multisite pain in the whole body and 10 % and 15 % in the musculoskeletal area during follow-up. The prediction ability of the machine learning approach with 48 predictive variables reached an AUC 0.65 at highest. With ML, a broad variety of predictors were identified, with up to 33 variables showing predictive power in girls and 13 in boys. Conclusions and Relevance Findings highlight that rather than any isolated variable, a variety of factors pose a risk for future multisite pain. More emphasis on holistic and multidisciplinary approaches is recommended to prevent multisite pain in adolescence. Key points Question What is the ability of machine learning approach to predict multisite pain incidence during adolescence? Findings With a random forest machine learning method, a broad variety of predictive physical, lifestyle and psychosocial factors were identified. Prediction ability reached AUC 0.65. Meaning The findings highlight that predictors of multisite pain incidence in adolescence are multifaceted, although the prediction ability of machine learning remained under clinical relevance (AUC <0.7). These findings support the adoption of holistic and multidisciplinary prevention approaches for multisite pain in adolescence in the future." @default.
• W4383313236 created "2023-07-07" @default.
• W4383313236 creator A5006324191 @default.
• W4383313236 creator A5014892292 @default.
• W4383313236 creator A5024350138 @default.
• W4383313236 creator A5045767004 @default.
• W4383313236 creator A5054812417 @default.
• W4383313236 creator A5076229128 @default.
• W4383313236 date "2023-07-06" @default.
• W4383313236 modified "2023-09-24" @default.
• W4383313236 title "Prediction of multisite pain incidence in adolescence using a machine learning approach" @default.
• W4383313236 cites W1480376833 @default.
• W4383313236 cites W1979933681 @default.
• W4383313236 cites W1995499655 @default.
• W4383313236 cites W2038443043 @default.
• W4383313236 cites W2058731333 @default.
• W4383313236 cites W2067256639 @default.
• W4383313236 cites W2080113925 @default.
• W4383313236 cites W2090736370 @default.
• W4383313236 cites W2148143831 @default.
• W4383313236 cites W2156590632 @default.
• W4383313236 cites W2559748025 @default.
• W4383313236 cites W2581156432 @default.
• W4383313236 cites W2758414429 @default.
• W4383313236 cites W2775898825 @default.
• W4383313236 cites W2795717160 @default.
• W4383313236 cites W2885855098 @default.
• W4383313236 cites W2911964244 @default.
• W4383313236 cites W2925073462 @default.
• W4383313236 cites W2979043067 @default.
• W4383313236 cites W3165555125 @default.
• W4383313236 cites W3189472744 @default.
• W4383313236 cites W4200390362 @default.
• W4383313236 cites W4283323613 @default.
• W4383313236 cites W4299689471 @default.
• W4383313236 cites W4303644819 @default.
• W4383313236 cites W4378213564 @default.
• W4383313236 doi "https://doi.org/10.1101/2023.07.04.23292222" @default.
• W4383313236 hasPublicationYear "2023" @default.
• W4383313236 type Work @default.
• W4383313236 citedByCount "0" @default.
• W4383313236 crossrefType "posted-content" @default.
• W4383313236 hasAuthorship W4383313236A5006324191 @default.
• W4383313236 hasAuthorship W4383313236A5014892292 @default.
• W4383313236 hasAuthorship W4383313236A5024350138 @default.
• W4383313236 hasAuthorship W4383313236A5045767004 @default.
• W4383313236 hasAuthorship W4383313236A5054812417 @default.
• W4383313236 hasAuthorship W4383313236A5076229128 @default.
• W4383313236 hasBestOaLocation W43833132361 @default.
• W4383313236 hasConcept C118552586 @default.
• W4383313236 hasConcept C120665830 @default.
• W4383313236 hasConcept C121332964 @default.
• W4383313236 hasConcept C142724271 @default.
• W4383313236 hasConcept C150966472 @default.
• W4383313236 hasConcept C1862650 @default.
• W4383313236 hasConcept C204787440 @default.
• W4383313236 hasConcept C23131810 @default.
• W4383313236 hasConcept C2776501849 @default.
• W4383313236 hasConcept C2908647359 @default.
• W4383313236 hasConcept C61511704 @default.
• W4383313236 hasConcept C71924100 @default.
• W4383313236 hasConcept C99454951 @default.
• W4383313236 hasConceptScore W4383313236C118552586 @default.
• W4383313236 hasConceptScore W4383313236C120665830 @default.
• W4383313236 hasConceptScore W4383313236C121332964 @default.
• W4383313236 hasConceptScore W4383313236C142724271 @default.
• W4383313236 hasConceptScore W4383313236C150966472 @default.
• W4383313236 hasConceptScore W4383313236C1862650 @default.
• W4383313236 hasConceptScore W4383313236C204787440 @default.
• W4383313236 hasConceptScore W4383313236C23131810 @default.
• W4383313236 hasConceptScore W4383313236C2776501849 @default.
• W4383313236 hasConceptScore W4383313236C2908647359 @default.
• W4383313236 hasConceptScore W4383313236C61511704 @default.
• W4383313236 hasConceptScore W4383313236C71924100 @default.
• W4383313236 hasConceptScore W4383313236C99454951 @default.
• W4383313236 hasLocation W43833132361 @default.
• W4383313236 hasOpenAccess W4383313236 @default.
• W4383313236 hasPrimaryLocation W43833132361 @default.
• W4383313236 hasRelatedWork W1580426574 @default.
• W4383313236 hasRelatedWork W1906133657 @default.
• W4383313236 hasRelatedWork W2089620537 @default.
• W4383313236 hasRelatedWork W2161284412 @default.
• W4383313236 hasRelatedWork W2275480158 @default.
• W4383313236 hasRelatedWork W2371214482 @default.
• W4383313236 hasRelatedWork W2788561295 @default.
• W4383313236 hasRelatedWork W3012255507 @default.
• W4383313236 hasRelatedWork W3095306390 @default.
• W4383313236 hasRelatedWork W4238089809 @default.
• W4383313236 isParatext "false" @default.
• W4383313236 isRetracted "false" @default.
• W4383313236 workType "article" @default.