Prevalence of Clinically Significant Improvement Following Total Knee Replacement
Bibliographic record
Abstract
OBJECTIVE: Although total knee replacement (TKR) has a high reported success rate, the pain relief and functional improvement after surgery vary. The purpose of our retrospective cohort study was to determine the prevalence of patients showing no clinically important improvement 1 year after TKR, and patient factors that may predict this outcome. METHODS: We reviewed primary TKR registry data that were collected from 2 academic hospitals: the Toronto Western Hospital and the Hamilton Health Sciences Henderson Hospital in Ontario, Canada. Relevant covariates including demographic data, body mass index, and comorbidity were recorded. Knee joint pain and functional status were assessed at baseline and at 1-year followup with the Western Ontario McMaster University Osteoarthritis Index (WOMAC) and Oxford Knee Score (OKS) to measure the change using the minimal clinically important difference (MCID). Logistic regression modeling was used to identify the predictors of interest. RESULTS: Overall, 11.7% (373/3177) of patients reported no clinically important improvement 1 year after surgery. Logistic regression modeling showed that a greater patient age independently predicted no clinically important improvement on the WOMAC scale 1 year after surgery (p = 0.0003), while being male independently predicted no clinically important improvement on the OKS 1 year after surgery (p = 0.008). CONCLUSION: Awareness of the prevalence of patients who may show no clinically important improvement and factors that predict this outcome will help patients and surgeons set realistic expectations of surgery.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".