Patterns of Functional Improvement After Revision Knee Arthroplasty
Bibliographic record
Abstract
BACKGROUND: Despite the increase in the number of total knee arthroplasty revisions, outcomes of such surgery and their correlates are poorly understood. The aim of this study was to characterize patterns of functional improvement after revision total knee arthroplasty over a two-year period and to investigate factors that affect such improvement patterns. METHODS: Three hundred and eight patients in need of revision surgery were enrolled into the study, conducted at seventeen centers, and 221 (71.8%) were followed for two years. Short Form-36 (SF-36), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and Lower-Extremity Activity Scale (LEAS) scores were collected at baseline and every six months for two years postoperatively. A piecewise general linear mixed model, which models correlation between repeated measures and estimates separate slopes for different follow-up time periods, was employed to examine functional improvement patterns. RESULTS: Separate regression slopes were estimated for the zero to twelve-month and the twelve to twenty-four-month periods. The slopes for zero to twelve months showed significant improvement in all measures in the first year. The slopes for twelve to twenty-four months showed deterioration in the scores of the WOMAC pain subscale (slope = 0.67 +/- 0.21, p < 0.01) and function subscale (slope = 1.66 +/- 0.63, p < 0.05), whereas the slopes of the other measures had plateaued. A higher number of comorbidities was consistently the strongest deterrent of functional improvement across measures. The modes of failure of the primary total knee arthroplasty were instrument-specific predictors of outcome (for example, tibial bone lysis affected only the SF-36 physical component score [coefficient = -5.46 +/- 1.91, p < 0.01], while malalignment affected both the SF-36 physical component score [coefficient = 5.41 +/- 2.35, p < 0.05] and the LEAS score [coefficient = 1.42 +/- 0.69, p < 0.05]). Factors related to the surgical technique did not predict outcomes. CONCLUSIONS: The onset of worsening pain and knee-specific function in the second year following revision total knee arthroplasty indicates the need to closely monitor patients, irrespective of the mode of failure of the primary procedure or the surgical technique for the revision. This information may be especially important for patients with multiple comorbidities.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".