A clinical prediction rule for predicting a delay in quality of life recovery at 1 month after total knee arthroplasty: A decision tree model

Tanaka, Shigeharu; Amano, Tetsuya; Uchida, Shigehiro; Itô, Hideyuki; Morikawa, Shinya; Inoue, Yu; Tanaka, Ryo

doi:10.1016/j.jos.2020.04.010

Cited by 9 publications

(6 citation statements)

References 27 publications

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“…From the 79 studies included, 50 investigated individuals with knee OA, 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 21 PFP, 14 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 ,…”

Section: Resultsunclassified

There is more to the knee joint than just the quadriceps: A systematic review with meta-analysis and evidence gap map of hamstring strength, flexibility, and morphology in individuals with gradual-onset knee disorders

Lopes,

Waiteman,

Priore

et al. 2024

Journal of Sport and Health Science

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Section: Resultsunclassified

There is more to the knee joint than just the quadriceps: A systematic review with meta-analysis and evidence gap map of hamstring strength, flexibility, and morphology in individuals with gradual-onset knee disorders

Lopes,

Waiteman,

Priore

et al. 2024

Journal of Sport and Health Science

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“…For example, the DT model is a map that shows possible outcomes based on a series of related decisions, using algorithms to predict the degree of obesity of individuals [16]. BMI tests are a key indicator for predicting body fatness [17], so in this work, we seek to develop a model that can be used as a predictive index of obesity [18]. However, it is important to note that external validation is vital, as it represents the most optimal situation [19].…”

Section: Introductionmentioning

confidence: 99%

Predicting Obesity in Nutritional Patients using Decision Tree Modeling

Iparraguirre-Villanueva,

Mirano-Portilla,

Gamarra-Mendoza

et al. 2024

IJACSA

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Obesity has become a widespread problem that affects not only physical well-being but also mental health. To address this problem and provide solutions, Machine Learning (ML) technology tools are being applied. Studies are currently being developed to improve the prediction of obesity. This study aimed to predict obesity levels in nutritional patients by analyzing their physical and dietary habits using the Decision Tree (DT) model. For the development of this work, we chose to use the CRISP-DM framework to follow the development in an organized way, thus achieving a better understanding of the data and describing, evaluating, and analyzing the results. The results of this work yielded metrics with significant values for predicting obesity: so much so that the accuracy rate was 92.89%, the sensitivity rate was 94% and the F1 score was 93%. Likewise, accuracy metrics above 88% were obtained for each level of obesity, demonstrating the effectiveness of the DT model in predicting this type of task. Finally, the results demonstrate that the DT model is effective in predicting obesity, with significant results that motivate further research to continue improving accuracy in this type of task.

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“…CPRs are widely used in rehabilitative medicine to assist in identifying subgroups of patients whose baseline characteristics indicate a high likelihood of responding to a particular intervention. Many CPRs exist in the field of musculoskeletal rehabilitation [59][60][61][62][63][64][65][66] but few exist for knee OA patients. This methodological strategy can be useful, as it can be directly applicable to individual patients.…”

Section: Introductionmentioning

confidence: 99%

“…A CPR indicates the accuracy, expressed as a sensitivity, specificity, or likelihood ratio (LR), of each individual predictor and of a group of the most potent predictors to identify patients whose response to treatment exceeds a certain threshold for success. CPRs have been used to create treatment-based classifications for patients that can be easily applied to clinical settings [59,[61][62][63][64][65][66]69], and the research methodological standards used to develop CPRs are well established [70]. The first phase, derivation, involves the identification of factors with predictive power and require longitudinal data [53,61].…”

Section: Introductionmentioning

confidence: 99%

Development of a Clinical Prediction Rule for Treatment Success with Transcranial Direct Current Stimulation for Knee Osteoarthritis Pain: A Secondary Analysis of a Double-Blind Randomized Controlled Trial

et al. 2022

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The study’s objective was to develop a clinical prediction rule that predicts a clinically significant analgesic effect on chronic knee osteoarthritis pain after transcranial direct current stimulation treatment. This is a secondary analysis from a double-blind randomized controlled trial. Data from 51 individuals with chronic knee osteoarthritis pain and an impaired descending pain inhibitory system were used. The intervention comprised a 15-session protocol of anodal primary motor cortex transcranial direct current stimulation. Treatment success was defined by the Western Ontario and McMaster Universities’ Osteoarthritis Index pain subscale. Accuracy statistics were calculated for each potential predictor and for the final model. The final logistic regression model was statistically significant (p < 0.01) and comprised five physical and psychosocial predictor variables that together yielded a positive likelihood ratio of 14.40 (95% CI: 3.66–56.69) and an 85% (95%CI: 60–96%) post-test probability of success. This is the first clinical prediction rule proposed for transcranial direct current stimulation in patients with chronic pain. The model underscores the importance of both physical and psychosocial factors as predictors of the analgesic response to transcranial direct current stimulation treatment. Validation of the proposed clinical prediction rule should be performed in other datasets.

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A clinical prediction rule for predicting a delay in quality of life recovery at 1 month after total knee arthroplasty: A decision tree model

Cited by 9 publications

References 27 publications

There is more to the knee joint than just the quadriceps: A systematic review with meta-analysis and evidence gap map of hamstring strength, flexibility, and morphology in individuals with gradual-onset knee disorders

There is more to the knee joint than just the quadriceps: A systematic review with meta-analysis and evidence gap map of hamstring strength, flexibility, and morphology in individuals with gradual-onset knee disorders

Predicting Obesity in Nutritional Patients using Decision Tree Modeling

Development of a Clinical Prediction Rule for Treatment Success with Transcranial Direct Current Stimulation for Knee Osteoarthritis Pain: A Secondary Analysis of a Double-Blind Randomized Controlled Trial

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