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
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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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 | 0.004 | 0.013 |
| 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 | 0.000 |
| 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".