Variation in age and physical status prior to total knee and hip replacement surgery: A comparison of centers in Australia and Europe
Bibliographic record
Abstract
OBJECTIVE: To investigate whether variation exists in the preoperative age, pain, stiffness, and physical function of people undergoing total knee replacement (TKR) and total hip replacement (THR) at several centers in Australia and Europe. METHODS: Individual Western Ontario and McMaster Universities Osteoarthritis Index data (range 0-100, where 0 = best and 100 = worst) collected within 6 weeks prior to primary TKR and THR were extracted from 16 centers (n = 2,835) according to specified eligibility criteria. Analysis of covariance was used to evaluate differences in pain, stiffness, and physical function between centers, with adjustment for age and sex. RESULTS: There was marked variation in the age of people undergoing surgery between the centers (TKR mean age 67-73 years; F[6,1004] = 4.21, P < 0.01, and THR mean age 63-72 years; F[14,1807] = 7.27, P < 0.01). Large differences in preoperative status were observed between centers, most notably for pain (TKR adjusted mean pain 52.5-61.1; F[6,1002] = 4.26, P < 0.01, and THR adjusted mean pain 49.2-65.7; F[14,1802] = 8.44, P < 0.01) and physical function (TKR adjusted mean function 52.7-61.4; F[6,1002] = 5.27, P < 0.01, and THR adjusted mean function 53.3-71.0; F[14,1802] = 6.71, P < 0.01). Large effect sizes (up to 0.98) reflect the magnitude of variation between centers and highlight the clinical relevance of these findings. CONCLUSION: The large variations in age and preoperative status indicate substantial differences in the timing of joint replacement across the centers studied, with potential for compromised surgical outcomes due to premature or delayed surgery. Possible contributing factors include patient preferences, the absence of concrete indications for surgery, and the capacity of the health care systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".