Development and internal validation of machine learning algorithms to predict patient satisfaction after total hip arthroplasty
Bibliographic record
Abstract
BACKGROUND: Patient satisfaction is a unique and important measure of success after total hip arthroplasty (THA). Our study aimed to evaluate the use of machine learning (ML) algorithms to predict patient satisfaction after THA. METHODS: Prospectively collected data of 1508 primary THAs performed between 2006 and 2018 were extracted from our joint replacement registry and split into training (80%) and test (20%) sets. Supervised ML algorithms (Random Forest, Extreme Gradient Boosting, Support Vector Machines, Logistic LASSO) were developed with the training set, using patient demographics, comorbidities and preoperative patient reported outcome measures (PROMs) (Short Form-36 [SF-36], physical component summary [PCS] and mental component summary [MCS], Western Ontario and McMaster's Universities Osteoarthritis Index [WOMAC] and Oxford Hip Score [OHS]) to predict patient satisfaction at 2 years postoperatively. Predictive performance was evaluated using the independent test set. RESULTS: Preoperative models demonstrated fair discriminative ability in predicting patient satisfaction, with the LASSO model achieving a maximum AUC of 0.76. Permutation importance revealed that the most important predictors of dissatisfaction were (1) patient's age, (2) preoperative WOMAC, (3) number of comorbidities, (4) preoperative MCS, (5) previous lumbar spine surgery, and (6) low BMI (< 18.5). CONCLUSION: Machine learning algorithms demonstrated fair discriminative ability in predicting patient satisfaction after THA. We have identified modifiable and non-modifiable predictors of postoperative satisfaction which could enhance preoperative counselling and improve health optimization prior to THA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".