Feel, move, or walk? Which has a greater contribution to functioning in total knee arthroplasty? A comparative study between two instrumentations based on a classification and regression tree
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: This study aimed to know which variables most contribute to the functioning acquired in the third month using the Western Ontario and McMaster Universities Arthritis Index (WOMAC) and a multivariate analysis through classification and regression tree (CRT), comparing the conventional instrumentation (CI), and patient-specific instrumentation (PSI). Methods: This is an observational and retrospective study. The sample consisted of 252 patients, 68 receiving CI (27.0%) and 184 receiving PSI (73.0%). The functional variables of the study were: knee pain, passive flexion and extension, gait distance and the domains of the WOMAC index. Results: The CRT method identified that the only explanatory variable that contributed to the highest functioning in the CI group (13.2 in the WOMAC) was pain in the third month with a value ≤2.5 in the visual analog scale (VAS). In the PSI group, the variable that best explained functioning was pain in the first postoperative month (VAS ≤4.5), with the best functional result (2.8 in WOMAC) referring to the patients who walked >320.5 m in the 6-minute walk test in the first month and who had flexion of >112.5 in the third month. Conclusions: Feeling pain is the variable with the most significant explanatory power for the results achieved in functioning at the third month, regardless of the arthroplasty instrumentation employed. Moving the knee in higher flexion ranges and obtaining higher mean values of gait speed also positively influences functioning in patients subjected to PSI.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it