FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387120003> ?p ?o ?g. }

• W4387120003 endingPage "6232" @default.
• W4387120003 startingPage "6232" @default.
• W4387120003 abstract "This study aims to compare the variable selection strategies of different machine learning (ML) and statistical algorithms in the prognosis of neck pain (NP) recovery. A total of 3001 participants with NP were included. Three dichotomous outcomes of an improvement in NP, arm pain (AP), and disability at 3 months follow-up were used. Twenty-five variables (twenty-eight parameters) were included as predictors. There were more parameters than variables, as some categorical variables had >2 levels. Eight modelling techniques were compared: stepwise regression based on unadjusted p values (stepP), on adjusted p values (stepPAdj), on Akaike information criterion (stepAIC), best subset regression (BestSubset) least absolute shrinkage and selection operator [LASSO], Minimax concave penalty (MCP), model-based boosting (mboost), and multivariate adaptive regression splines (MuARS). The algorithm that selected the fewest predictors was stepPAdj (number of predictors, p = 4 to 8). MuARS was the algorithm with the second fewest predictors selected (p = 9 to 14). The predictor selected by all algorithms with the largest coefficient magnitude was having undergone a neuroreflexotherapy intervention for NP (β = from 1.987 to 2.296) and AP (β = from 2.639 to 3.554), and Imaging findings: spinal stenosis (β = from -1.331 to -1.763) for disability. Stepwise regression based on adjusted p-values resulted in the sparsest models, which enhanced clinical interpretability. MuARS appears to provide the optimal balance between model sparsity whilst retaining high predictive performance across outcomes. Different algorithms produced similar performances but resulted in a different number of variables selected. Rather than relying on any single algorithm, confidence in the variable selection may be increased by using multiple algorithms." @default.
• W4387120003 created "2023-09-29" @default.
• W4387120003 creator A5050668877 @default.
• W4387120003 creator A5062046770 @default.
• W4387120003 creator A5066285962 @default.
• W4387120003 creator A5067248213 @default.
• W4387120003 date "2023-09-27" @default.
• W4387120003 modified "2023-10-18" @default.
• W4387120003 title "Automatic Variable Selection Algorithms in Prognostic Factor Research in Neck Pain" @default.
• W4387120003 cites W1933386641 @default.
• W4387120003 cites W1994682257 @default.
• W4387120003 cites W2009462809 @default.
• W4387120003 cites W2011267338 @default.
• W4387120003 cites W2052177264 @default.
• W4387120003 cites W2056748740 @default.
• W4387120003 cites W2062467241 @default.
• W4387120003 cites W2100741073 @default.
• W4387120003 cites W2121935706 @default.
• W4387120003 cites W2132129083 @default.
• W4387120003 cites W2142635246 @default.
• W4387120003 cites W2169779353 @default.
• W4387120003 cites W2191755155 @default.
• W4387120003 cites W2320230383 @default.
• W4387120003 cites W2604736517 @default.
• W4387120003 cites W2759600886 @default.
• W4387120003 cites W2770346636 @default.
• W4387120003 cites W2780157115 @default.
• W4387120003 cites W2782176193 @default.
• W4387120003 cites W2804586924 @default.
• W4387120003 cites W2805370464 @default.
• W4387120003 cites W2890947206 @default.
• W4387120003 cites W2898646011 @default.
• W4387120003 cites W2905925844 @default.
• W4387120003 cites W2934849359 @default.
• W4387120003 cites W2947966515 @default.
• W4387120003 cites W2948009788 @default.
• W4387120003 cites W2949052936 @default.
• W4387120003 cites W2998234081 @default.
• W4387120003 cites W3008867523 @default.
• W4387120003 cites W3013381570 @default.
• W4387120003 cites W3040782208 @default.
• W4387120003 cites W3092531627 @default.
• W4387120003 cites W3106108064 @default.
• W4387120003 cites W3111016709 @default.
• W4387120003 cites W3175417087 @default.
• W4387120003 cites W3186140673 @default.
• W4387120003 cites W3201437744 @default.
• W4387120003 cites W4220944089 @default.
• W4387120003 cites W4220946186 @default.
• W4387120003 doi "https://doi.org/10.3390/jcm12196232" @default.
• W4387120003 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37834877" @default.
• W4387120003 hasPublicationYear "2023" @default.
• W4387120003 type Work @default.
• W4387120003 citedByCount "0" @default.
• W4387120003 crossrefType "journal-article" @default.
• W4387120003 hasAuthorship W4387120003A5050668877 @default.
• W4387120003 hasAuthorship W4387120003A5062046770 @default.
• W4387120003 hasAuthorship W4387120003A5066285962 @default.
• W4387120003 hasAuthorship W4387120003A5067248213 @default.
• W4387120003 hasBestOaLocation W43871200031 @default.
• W4387120003 hasConcept C105795698 @default.
• W4387120003 hasConcept C11413529 @default.
• W4387120003 hasConcept C119857082 @default.
• W4387120003 hasConcept C126322002 @default.
• W4387120003 hasConcept C136764020 @default.
• W4387120003 hasConcept C142724271 @default.
• W4387120003 hasConcept C148483581 @default.
• W4387120003 hasConcept C152877465 @default.
• W4387120003 hasConcept C154945302 @default.
• W4387120003 hasConcept C170964787 @default.
• W4387120003 hasConcept C189285262 @default.
• W4387120003 hasConcept C203868755 @default.
• W4387120003 hasConcept C204787440 @default.
• W4387120003 hasConcept C2781038967 @default.
• W4387120003 hasConcept C2781067378 @default.
• W4387120003 hasConcept C33923547 @default.
• W4387120003 hasConcept C37616216 @default.
• W4387120003 hasConcept C41008148 @default.
• W4387120003 hasConcept C46686674 @default.
• W4387120003 hasConcept C5274069 @default.
• W4387120003 hasConcept C71924100 @default.
• W4387120003 hasConcept C83546350 @default.
• W4387120003 hasConceptScore W4387120003C105795698 @default.
• W4387120003 hasConceptScore W4387120003C11413529 @default.
• W4387120003 hasConceptScore W4387120003C119857082 @default.
• W4387120003 hasConceptScore W4387120003C126322002 @default.
• W4387120003 hasConceptScore W4387120003C136764020 @default.
• W4387120003 hasConceptScore W4387120003C142724271 @default.
• W4387120003 hasConceptScore W4387120003C148483581 @default.
• W4387120003 hasConceptScore W4387120003C152877465 @default.
• W4387120003 hasConceptScore W4387120003C154945302 @default.
• W4387120003 hasConceptScore W4387120003C170964787 @default.
• W4387120003 hasConceptScore W4387120003C189285262 @default.
• W4387120003 hasConceptScore W4387120003C203868755 @default.
• W4387120003 hasConceptScore W4387120003C204787440 @default.
• W4387120003 hasConceptScore W4387120003C2781038967 @default.
• W4387120003 hasConceptScore W4387120003C2781067378 @default.
• W4387120003 hasConceptScore W4387120003C33923547 @default.

• next