Predictors of Participation in Sports After Hip and Knee Arthroplasty
Bibliographic record
Abstract
BACKGROUND: While the primary objective of joint arthroplasty is to improve patient quality of life, pain, and function, younger active patients often demand a return to higher function that includes sporting activity. Knowledge of rates and predictors of return to sports will help inform expectations in patients anticipating return to sports after joint arthroplasty. QUESTIONS/PURPOSES: We measured the rate of sports participation at 1 year using the UCLA activity score and explored 11 variables, including choice of procedure/prosthesis, that might predict return to a high level of sporting activity, when controlling for potential confounding variables. METHODS: We retrospectively evaluated 736 patients who underwent primary metal-on-polyethylene THA, metal-on-metal THA, hip resurfacing arthroplasty, revision THA, primary TKA, unicompartmental knee arthroplasty, and revision TKA between May 2005 and June 2007. We obtained UCLA activity scores on all patients; we defined high activity as a UCLA score of 7 or more. We evaluated patient demographics (age, sex, BMI, comorbidity), quality of life (WOMAC score, Oxford Hip Score, SF-12 score), and surgeon- and procedural/implant-specific variables to identify factors associated with postoperative activity score. Minimum followup was 11 months (mean, 12.1 months; range, 11-13 months). RESULTS: Preoperative UCLA activity score, age, male sex, and BMI predicted high activity scores. The type of operation and implant characteristics did not predict return to high activity sports. CONCLUSIONS: Our data suggest patient-specific factors predict postoperative activity rather than factors specific to type of surgery, implant, or surgeon factors. LEVEL OF EVIDENCE: Level II, prognostic study. See the Guidelines for Authors for a complete description of levels of evidence.
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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 | 0.003 |
| 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 | 0.001 |
| 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".