Wearable sensor-based measures of step-up transfers are supplementary to patient-reported outcome measures following total joint arthroplasty
Bibliographic record
Abstract
Purpose This study investigated the longitudinal assessment of step-up performance in patients undergoing total joint arthroplasty (TJA) and correlation with subjective patient reported outcome measures (PROMs).Methods In this sub-analysis of the ADAPT study, PROMs were assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Block step-up (BS) transfers were assessed by wearable-derived measures of time. 76 patients undergoing TJA were included. Subgroups were formed isolating the worst performing quartile (low functioning (LF)) from the high functioning (HF), and outcomes were compared–Results One-year post-surgery, WOMAC function demonstrated strong correlations to WOMAC pain (Pearson’s r = 0.67–0.84) and moderate correlations to BS performance (Pearson’s r = 0.31–0.54). Both WOMAC and BS significantly improved with a larger effect size for the HF subgroup (0.62 vs. 0.43; p < 0.05). Patients designated to the LF subgroup at 3 months had increased odds of representing the LF subgroup at 12 months (WOMAC = 19; BS = 4). WOMAC defined 18 LF patients at 12 months follow-up. BS performance identified 9 additional LF patients.Conclusions WOMAC function scores seem pain dominated. Measures of BS performance allow assessment of otherwise hidden residual functional impairment. Lower functioning 3 months post-surgery is predictive of longer-term impairment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".