Research graph
References from Development and validation of machine learning models for predicting rehabilitation non-response after knee arthroplasty. Local targets link to admitted publications; unresolved targets remain external evidence.
Knee replacement
10.1016/s0140-6736(11)60752-6 · 2012 · External reference
Introduction to the indications and procedures
10.1007/978-3-662-55918-5_1 · 2018 · External reference
Impact of total knee replacement practice: cost effectiveness analysis of data from the osteoarthritis initiative
10.1136/bmj.j1131 · 2017 · External reference
Knee replacement
10.1016/s0140-6736(18)32344-4 · 2018 · External reference
Functions, disabilities and perceived health in the first year after total knee arthroplasty; a prospective cohort study
10.1186/s12891-018-2159-7 · 2018 · External reference
Assessment of outcomes of inpatient or clinic-based vs home-based rehabilitation after total knee arthroplasty: a systematic review and meta-analysis
10.1001/jamanetworkopen.2019.2810 · 2019 · External reference
Inpatient compared with home-based rehabilitation following primary unilateral total hip or knee replacement: a randomized controlled trial
10.2106/jbjs.g.01108 · 2008 · External reference
Determinants and outcomes of inpatient versus home based rehabilitation following elective hip and knee replacement
2000 · External reference
Physical function following total knee arthroplasty for osteoarthritis: a longitudinal systematic review with meta-analysis
10.2519/jospt.2024.12570 · 2025 · External reference
General and disease-specific health indicator changes associated with inpatient rehabilitation
10.1016/j.jamda.2020.05.034 · 2020 · External reference
Measurement of health status
10.1016/0197-2456(89)90005-6 · 1989 · External reference
Same but different? Exploring the role of patient-reported outcome measures and clinician-reported outcome measures in postoperative knee and hip arthroplasty rehabilitation
10.3390/jcm14072322 · 2025 · External reference
Associations between patient-reported and clinician-reported outcome measures in patients after traumatic injuries of the lower limb
10.3390/ijerph19053140 · 2022 · External reference
Patient-Reported outcome measures are not a valid proxy for patient satisfaction in total joint arthroplasty
10.1016/j.arth.2019.09.033 · 2020 · External reference
The WOMAC score can be reliably used to classify patient satisfaction after total knee arthroplasty
10.1007/s00167-018-4879-5 · 2018 · External reference
Patient satisfaction after total knee arthroplasty: who is satisfied and who is not?
10.1007/s11999-009-1119-9 · 2010 · External reference
Predicting patient dissatisfaction following joint replacement surgery
10.3899/jrheum.080295 · 2008 · External reference
Patient dissatisfaction after primary total joint arthroplasty: the patient perspective
10.1016/j.arth.2019.01.075 · 2019 · External reference
Predicting whether patients will achieve minimal clinically important differences following hip or knee arthroplasty
10.1302/2046-3758.129.bjr-2023-0070.r2 · 2023 · External reference
Can machine learning algorithms predict which patients will achieve minimally clinically important differences from total joint arthroplasty?
10.1097/corr.0000000000000687 · 2019 · External reference
Development of machine learning algorithms to predict clinically meaningful improvement for the patient-reported health state after total hip arthroplasty
10.1016/j.arth.2020.03.019 · 2020 · External reference
Patient-reported health outcomes after total hip and knee surgery in a Dutch university hospital setting: results of twenty years clinical registry
10.1186/s12891-017-1455-y · 2017 · External reference
Patient-Reported outcomes — are they living up to their potential?
10.1056/nejmp1702978 · 2017 · External reference
Patient-Reported outcomes for function and pain in total knee arthroplasty patients
10.1097/nnr.0000000000000602 · 2022 · External reference
Psychological factors are important to return to pre-injury sport activity after anterior cruciate ligament reconstruction: expect and motivate to satisfy
10.1007/s00167-016-4294-8 · 2017 · External reference
Machine learning approaches to predict rehabilitation success based on clinical and patient-reported outcome measures
10.1016/j.imu.2021.100598 · 2021 · External reference
Unresolved reference
External reference
Predicting patient-reported outcomes following hip and knee replacement surgery using supervised machine learning
10.1186/s12911-018-0731-6 · 2019 · External reference
Artificial intelligence for clinically meaningful outcome prediction in orthopedic research: current applications and limitations
10.1007/s12178-024-09893-z · 2024 · External reference
The impact of machine learning on total joint arthroplasty patient outcomes: a systemic review
10.1016/j.arth.2022.10.039 · 2023 · External reference
Can minimal clinically important differences in patient reported outcome measures be predicted by machine learning in patients with total knee or hip arthroplasty? A systematic review
10.1186/s12911-022-01751-7 · 2022 · External reference
Using artificial intelligence to predict patient outcomes from patient-reported outcome measures: a scoping review
10.1186/s12955-025-02365-z · 2025 · External reference
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · 2024 · External reference
Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-cluster checklist
10.1136/bmj-2022-071018 · 2023 · External reference
Unresolved reference
2024 · External reference
Unresolved reference
2018 · External reference
Unresolved reference
External reference
Measurement properties of the EQ-5D-5L compared to the EQ-5D-3L across eight patient groups: a multi-country study
10.1007/s11136-012-0322-4 · 2013 · External reference
The timed “up & go”: a test of basic functional mobility for frail elderly persons
10.1111/j.1532-5415.1991.tb01616.x · 1991 · External reference
Responsiveness of physical function outcomes following physiotherapy intervention for osteoarthritis of the knee: an outcome comparison study
10.1016/j.physio.2010.03.002 · 2011 · External reference
Relationship between timed ‘up and go’ and gait time in an elderly orthopaedic rehabilitation population
10.1191/026921500675545616 · 2000 · External reference
Relationship among performance on stair ambulation, functional reach, and timed up and go tests in older adults
1998 · External reference
Target setting for lower limb joint surgery using the timed up and go test in patients with rheumatoid arthritis: a prospective cohort study
10.1111/1756-185x.13394 · 2018 · External reference
Balance assessment in patients with peripheral arthritis: applicability and reliability of some clinical assessments
10.1002/pri.228 · 2001 · External reference
Mind the MIC: large variation among populations and methods
10.1016/j.jclinepi.2009.08.010 · 2010 · External reference
Defining clinically meaningful change in health-related quality of life
10.1016/s0895-4356(03)00044-1 · 2003 · External reference
Clinimetrics corner: a closer look at the minimal clinically important difference (MCID)
10.1179/2042618612y.0000000001 · 2012 · External reference
Practical issues encountered while determining minimal clinically important difference in patient-reported outcomes
10.1186/s12955-020-01398-w · 2020 · External reference
The concept of clinically meaningful difference in health-related quality of life research: how meaningful is it?
10.2165/00019053-200018050-00001 · 2000 · External reference
Responsiveness and minimal clinically important difference for pain and disability instruments in low back pain patients
10.1186/1471-2474-7-82 · 2006 · External reference
Timed up and go (TUG) test: normative reference values for ages 20 to 59 years and relationships with physical and mental health risk factors
10.1177/2150131916659282 · 2017 · External reference
Unresolved reference
External reference
Ensemble methods: bagging and random forests
10.1038/nmeth.4438 · 2017 · External reference
Automated machine learning: past, present and future
10.1007/s10462-024-10726-1 · 2024 · External reference
Geographic and temporal validity of prediction models: different approaches were useful to examine model performance
10.1016/j.jclinepi.2016.05.007 · 2016 · External reference
Assessing discriminative ability of risk models in clustered data
10.1186/1471-2288-14-5 · 2014 · External reference
Three myths about risk thresholds for prediction models
10.1186/s12916-019-1425-3 · 2019 · External reference
Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance
10.1016/j.landig.2025.100916 · 2025 · External reference
Verification of forecasts expressed in terms of probability
10.1175/1520-0493(1950)078<0001:vofeit>2.0.co;2 · 1950 · External reference
The integrated calibration Index (ICI) and related metrics for quantifying the calibration of logistic regression models
10.1002/sim.8281 · 2019 · External reference
Topic group ‘evaluating diagnostic tests and prediction models’ of the STRATOS initiative. Calibration: the achilles heel of predictive analytics
10.1186/s12916-019-1466-7 · 2019 · External reference
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach
10.2307/2531595 · 1988 · External reference
A unified approach to interpreting model predictions
2017 · External reference
Decision curve analysis: a novel method for evaluating prediction models
10.1177/0272989x06295361 · 2006 · External reference
Development of machine learning algorithms to predict achievement of minimal clinically important difference for the KOOS-PS following total knee arthroplasty
10.1002/jor.25125 · 2022 · External reference
Comparison of patient- and clinician-reported outcome measures in lower back rehabilitation: introducing a new integrated performance measure (t2D)
10.1007/s11136-021-02905-2 · 2022 · External reference
Mathematical coupling may account for the association between baseline severity and minimally important difference values
10.1016/j.jclinepi.2009.10.004 · 2010 · External reference
A development of machine learning models to preoperatively predict insufficient clinical improvement after total knee arthroplasty
10.1186/s13018-025-06206-z · 2025 · External reference
Psychological predictors of anterior cruciate ligament reconstruction outcomes: a systematic review
10.1007/s00167-013-2699-1 · 2015 · External reference
Common method bias: it’s bad, it’s complex, it’s widespread, and it’s not easy to fix
10.1146/annurev-orgpsych-110721-040030 · 2024 · External reference
Trends in the conduct and reporting of clinical prediction model development and validation: a systematic review
10.1093/jamia/ocac002 · 2022 · External reference
External validation of deep learning algorithms for radiologic diagnosis: a systematic review
10.1148/ryai.210064 · 2022 · External reference
Evaluation of clinical prediction models (part 1): from development to external validation
10.1136/bmj-2023-074819 · 2024 · External reference
Clinical implementation of predictive models embedded within electronic health record systems: a systematic review
10.3390/informatics7030025 · 2020 · External reference
Fixing the leaky pipe: how to improve the uptake of patient-reported outcomes-based prognostic and predictive models in cancer clinical practice
10.1200/cci.23.00070 · 2023 · External reference
Assessment of outcomes of inpatient or clinic-based vs home-based rehabilitation after total knee arthroplasty: a systematic review and meta-analysis
10.1001/jamanetworkopen.2019.2810 · ExternalCitation · doi-reference
Development of machine learning algorithms to predict achievement of minimal clinically important difference for the KOOS-PS following total knee arthroplasty
10.1002/jor.25125 · ExternalCitation · doi-reference
Balance assessment in patients with peripheral arthritis: applicability and reliability of some clinical assessments
10.1002/pri.228 · ExternalCitation · doi-reference
The integrated calibration Index (ICI) and related metrics for quantifying the calibration of logistic regression models
10.1002/sim.8281 · ExternalCitation · doi-reference
Introduction to the indications and procedures
10.1007/978-3-662-55918-5_1 · ExternalCitation · doi-reference
Psychological predictors of anterior cruciate ligament reconstruction outcomes: a systematic review
10.1007/s00167-013-2699-1 · ExternalCitation · doi-reference
Psychological factors are important to return to pre-injury sport activity after anterior cruciate ligament reconstruction: expect and motivate to satisfy
10.1007/s00167-016-4294-8 · ExternalCitation · doi-reference
The WOMAC score can be reliably used to classify patient satisfaction after total knee arthroplasty
10.1007/s00167-018-4879-5 · ExternalCitation · doi-reference
Automated machine learning: past, present and future
10.1007/s10462-024-10726-1 · ExternalCitation · doi-reference
Measurement properties of the EQ-5D-5L compared to the EQ-5D-3L across eight patient groups: a multi-country study
10.1007/s11136-012-0322-4 · ExternalCitation · doi-reference
Comparison of patient- and clinician-reported outcome measures in lower back rehabilitation: introducing a new integrated performance measure (t2D)
10.1007/s11136-021-02905-2 · ExternalCitation · doi-reference
Patient satisfaction after total knee arthroplasty: who is satisfied and who is not?
10.1007/s11999-009-1119-9 · ExternalCitation · doi-reference
Artificial intelligence for clinically meaningful outcome prediction in orthopedic research: current applications and limitations
10.1007/s12178-024-09893-z · ExternalCitation · doi-reference
Measurement of health status
10.1016/0197-2456(89)90005-6 · ExternalCitation · doi-reference
Patient dissatisfaction after primary total joint arthroplasty: the patient perspective
10.1016/j.arth.2019.01.075 · ExternalCitation · doi-reference
Patient-Reported outcome measures are not a valid proxy for patient satisfaction in total joint arthroplasty
10.1016/j.arth.2019.09.033 · ExternalCitation · doi-reference
Development of machine learning algorithms to predict clinically meaningful improvement for the patient-reported health state after total hip arthroplasty
10.1016/j.arth.2020.03.019 · ExternalCitation · doi-reference
The impact of machine learning on total joint arthroplasty patient outcomes: a systemic review
10.1016/j.arth.2022.10.039 · ExternalCitation · doi-reference
Machine learning approaches to predict rehabilitation success based on clinical and patient-reported outcome measures
10.1016/j.imu.2021.100598 · ExternalCitation · doi-reference
General and disease-specific health indicator changes associated with inpatient rehabilitation
10.1016/j.jamda.2020.05.034 · ExternalCitation · doi-reference
Mind the MIC: large variation among populations and methods
10.1016/j.jclinepi.2009.08.010 · ExternalCitation · doi-reference
Mathematical coupling may account for the association between baseline severity and minimally important difference values
10.1016/j.jclinepi.2009.10.004 · ExternalCitation · doi-reference
Geographic and temporal validity of prediction models: different approaches were useful to examine model performance
10.1016/j.jclinepi.2016.05.007 · ExternalCitation · doi-reference
Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance
10.1016/j.landig.2025.100916 · ExternalCitation · doi-reference
Responsiveness of physical function outcomes following physiotherapy intervention for osteoarthritis of the knee: an outcome comparison study
10.1016/j.physio.2010.03.002 · ExternalCitation · doi-reference
Knee replacement
10.1016/s0140-6736(11)60752-6 · ExternalCitation · doi-reference
Knee replacement
10.1016/s0140-6736(18)32344-4 · ExternalCitation · doi-reference
Defining clinically meaningful change in health-related quality of life
10.1016/s0895-4356(03)00044-1 · ExternalCitation · doi-reference
Ensemble methods: bagging and random forests
10.1038/nmeth.4438 · ExternalCitation · doi-reference
Patient-Reported outcomes — are they living up to their potential?
10.1056/nejmp1702978 · ExternalCitation · doi-reference
Trends in the conduct and reporting of clinical prediction model development and validation: a systematic review
10.1093/jamia/ocac002 · ExternalCitation · doi-reference
Can machine learning algorithms predict which patients will achieve minimally clinically important differences from total joint arthroplasty?
10.1097/corr.0000000000000687 · ExternalCitation · doi-reference
Patient-Reported outcomes for function and pain in total knee arthroplasty patients
10.1097/nnr.0000000000000602 · ExternalCitation · doi-reference
Target setting for lower limb joint surgery using the timed up and go test in patients with rheumatoid arthritis: a prospective cohort study
10.1111/1756-185x.13394 · ExternalCitation · doi-reference
The timed “up & go”: a test of basic functional mobility for frail elderly persons
10.1111/j.1532-5415.1991.tb01616.x · ExternalCitation · doi-reference
Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-cluster checklist
10.1136/bmj-2022-071018 · ExternalCitation · doi-reference
Evaluation of clinical prediction models (part 1): from development to external validation
10.1136/bmj-2023-074819 · ExternalCitation · doi-reference
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · ExternalCitation · doi-reference
Impact of total knee replacement practice: cost effectiveness analysis of data from the osteoarthritis initiative
10.1136/bmj.j1131 · ExternalCitation · doi-reference
Common method bias: it’s bad, it’s complex, it’s widespread, and it’s not easy to fix
10.1146/annurev-orgpsych-110721-040030 · ExternalCitation · doi-reference
External validation of deep learning algorithms for radiologic diagnosis: a systematic review
10.1148/ryai.210064 · ExternalCitation · doi-reference
Verification of forecasts expressed in terms of probability
10.1175/1520-0493(1950)078<0001:vofeit>2.0.co;2 · ExternalCitation · doi-reference
Decision curve analysis: a novel method for evaluating prediction models
10.1177/0272989x06295361 · ExternalCitation · doi-reference
Timed up and go (TUG) test: normative reference values for ages 20 to 59 years and relationships with physical and mental health risk factors
10.1177/2150131916659282 · ExternalCitation · doi-reference
Clinimetrics corner: a closer look at the minimal clinically important difference (MCID)
10.1179/2042618612y.0000000001 · ExternalCitation · doi-reference
Assessing discriminative ability of risk models in clustered data
10.1186/1471-2288-14-5 · ExternalCitation · doi-reference
Responsiveness and minimal clinically important difference for pain and disability instruments in low back pain patients
10.1186/1471-2474-7-82 · ExternalCitation · doi-reference
Patient-reported health outcomes after total hip and knee surgery in a Dutch university hospital setting: results of twenty years clinical registry
10.1186/s12891-017-1455-y · ExternalCitation · doi-reference
Functions, disabilities and perceived health in the first year after total knee arthroplasty; a prospective cohort study
10.1186/s12891-018-2159-7 · ExternalCitation · doi-reference
Predicting patient-reported outcomes following hip and knee replacement surgery using supervised machine learning
10.1186/s12911-018-0731-6 · ExternalCitation · doi-reference
Can minimal clinically important differences in patient reported outcome measures be predicted by machine learning in patients with total knee or hip arthroplasty? A systematic review
10.1186/s12911-022-01751-7 · ExternalCitation · doi-reference
Three myths about risk thresholds for prediction models
10.1186/s12916-019-1425-3 · ExternalCitation · doi-reference
Topic group ‘evaluating diagnostic tests and prediction models’ of the STRATOS initiative. Calibration: the achilles heel of predictive analytics
10.1186/s12916-019-1466-7 · ExternalCitation · doi-reference
Practical issues encountered while determining minimal clinically important difference in patient-reported outcomes
10.1186/s12955-020-01398-w · ExternalCitation · doi-reference
Using artificial intelligence to predict patient outcomes from patient-reported outcome measures: a scoping review
10.1186/s12955-025-02365-z · ExternalCitation · doi-reference
A development of machine learning models to preoperatively predict insufficient clinical improvement after total knee arthroplasty
10.1186/s13018-025-06206-z · ExternalCitation · doi-reference
Relationship between timed ‘up and go’ and gait time in an elderly orthopaedic rehabilitation population
10.1191/026921500675545616 · ExternalCitation · doi-reference
Fixing the leaky pipe: how to improve the uptake of patient-reported outcomes-based prognostic and predictive models in cancer clinical practice
10.1200/cci.23.00070 · ExternalCitation · doi-reference
Predicting whether patients will achieve minimal clinically important differences following hip or knee arthroplasty
10.1302/2046-3758.129.bjr-2023-0070.r2 · ExternalCitation · doi-reference
Inpatient compared with home-based rehabilitation following primary unilateral total hip or knee replacement: a randomized controlled trial
10.2106/jbjs.g.01108 · ExternalCitation · doi-reference
The concept of clinically meaningful difference in health-related quality of life research: how meaningful is it?
10.2165/00019053-200018050-00001 · ExternalCitation · doi-reference
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach
10.2307/2531595 · ExternalCitation · doi-reference
Physical function following total knee arthroplasty for osteoarthritis: a longitudinal systematic review with meta-analysis
10.2519/jospt.2024.12570 · ExternalCitation · doi-reference
Associations between patient-reported and clinician-reported outcome measures in patients after traumatic injuries of the lower limb
10.3390/ijerph19053140 · ExternalCitation · doi-reference
Clinical implementation of predictive models embedded within electronic health record systems: a systematic review
10.3390/informatics7030025 · ExternalCitation · doi-reference
Same but different? Exploring the role of patient-reported outcome measures and clinician-reported outcome measures in postoperative knee and hip arthroplasty rehabilitation
10.3390/jcm14072322 · ExternalCitation · doi-reference
Predicting patient dissatisfaction following joint replacement surgery
10.3899/jrheum.080295 · ExternalCitation · doi-reference
