Impact of Preoperative and Incident Musculoskeletal Problematic Areas on Postoperative Outcomes After Total Knee Replacement
Bibliographic record
Abstract
OBJECTIVE: To examine impact of pre-existing and incident problematic musculoskeletal (MSK) areas after total knee replacement (TKR) on postoperative 60-month Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain/function scores. METHODS: Using data from a randomized controlled trial of subjects undergoing TKR for osteoarthritis, we assessed problematic MSK areas in six body regions before TKR and 12, 24, 36, and 48 months after TKR. We defined the following two variables: 1) density count (number of problematic MSK areas occurring after TKR; range 0-24) and 2) cumulative density count (problematic MSK areas both before and after TKR, categorized into four levels: no preoperative areas and density count of 0-1 [reference group]; no preoperative areas and density count of 2 or more; one or more preoperative areas and density count of 0-1; and one or more preoperative areas and density count of 2 or greater). We evaluated the associations between categorized 60-month WOMAC and cumulative density count by ordinal logistic regression. RESULTS: Among 230 subjects, 24% reported one or more preoperative problematic MSK area. After TKR, 75% reported a density count of 0 to 1; 25% reported a density count of 2 or more. Compared with the reference group, each cumulative density count category was associated with an increased odds of having a higher category of 60-month WOMAC pain score, as follows: 2.97 (95% confidence interval [CI], 1.48-5.98) for no preoperative problematic areas and density count of 2 or greater, 3.31 (95% CI, 1.64-6.66) for one or more preoperative problematic areas and density count of 0 to 1, and 2.85 (95% CI, 0.97-8.39) for one or more preoperative problematic areas and density count of 2 or greater. Similar associations were observed with 60-month WOMAC function score. CONCLUSION: In TKR recipients, the presence of problematic musculoskeletal areas beyond the index knee-preoperatively and/or postoperatively-was associated with worse 60-month WOMAC pain/function score.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".