Machine learning and wearable sensors at preoperative assessments: Functional recovery prediction to set realistic expectations for knee replacements
Bibliographic record
Abstract
Unmet expectations contribute to a high patient dissatisfaction rate following total knee replacement but clinicians currently do not have the tools to confidently adjust expectations. In this study, supervised machine learning was applied to multi-variate wearable sensor data from preoperative timed-up-and-go tests. Participants (n=82) were instrumented three months after surgery and patients showing relevant improvement were designated as "responders" while the remainder were labelled "maintainers". Support vector machine, naïve Bayes, and random forest binary classifiers were developed to distinguish patients using sensor-derived features. Accuracy, sensitivity, specificity, and area under the receiver-operator curve (AUC) were compared between models using ten-fold out-of-sample testing. A high performance using only sensor-derived functional metrics was obtained with a random forest model (accuracy = 0.76 ± 0.11, sensitivity = 0.87 ± 0.08, specificity = 0.57 ± 0.26, AUC = 0.80 ± 0.14) but highly sensitive models were observed using naïve Bayes and SVM models after including patient age, sex, and BMI into the feature set (accuracy = 0.72, 0.73 ± 0.09, 0.12; sensitivity = 0.94, 0.95 ± 0.11, 0.11; specificity = 0.35, 0.37 ± 0.20, 0.18; AUC = 0.80, 0.74 ± 0.07, 0.11; respectfully). Including select patient-reported subjective measures increased the top random forest performance slightly (accuracy = 0.80 ± 0.10, sensitivity = 0.91 ± 0.14, specificity = 0.62 ± 0.23, AUC = 0.86 ± 0.09). The current work has demonstrated that prediction models developed from preoperative sensor-derived functional metrics can reliably predict expected functional recovery following surgery and this can be used by clinicians to help set realistic patient expectations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 source (direct Gemma or distilled Codex), 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".