Patient Preferences for Surgical Treatment of Knee Osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Total knee arthroplasty (TKA) is a common treatment for end-stage knee osteoarthritis but is associated with increased complication rates compared with unicompartmental knee arthroplasty (UKA). UKA offers better functional outcomes but is associated with a higher risk of revision. The purpose of this study was to apply good-practice, stated-preference methods to quantify patient preferences for benefit-risk tradeoffs associated with arthroplasty treatments for end-stage knee osteoarthritis. METHODS: A discrete-choice experiment was developed with the following attributes: chance of complications, functional ability, awareness of the knee implant, and chance of needing another operation within 10 years. Patients included those aged 40 to 80 years with knee osteoarthritis. A pivot design filtered respondents into 1 of 2 surveys on the basis of self-reported functional ability (good compared with fair or poor) as measured by the Oxford Knee Score. Treatment-preference data were collected, and relative attribute-importance weights were estimated. RESULTS: Two hundred and fifty-eight completed survey instruments from 92 males and 164 females were analyzed, with 72 respondents in the good-function cohort and 186 in the fair/poor-function cohort. Patients placed the greatest value or relative importance on serious complications and rates of revision in both cohorts. Preference weights did not vary between cohorts for any attribute. In the good-function cohort, 42% of respondents chose TKA and 58% chose UKA. In the fair/poor-function cohort, 54% chose TKA and 46% chose UKA. CONCLUSIONS: Patient preferences for various treatment attributes varied among patients in a knee osteoarthritis population. Complication and revision rates were the most important factors to patients, suggesting that physicians should focus on these areas when discussing treatments. The proportion of patients who chose UKA suggests that the current trend of increased UKA utilization is aligned with patient preferences. CLINICAL RELEVANCE: Systematic elicitation of patient preferences for knee arthroplasty procedures, which lays out evidence-based risks and benefits of different treatments, indicates a larger subset of the knee osteoarthritis population may prefer UKA than would be suggested by the current rates of utilization of the procedure. Arthroplasty treatment should align with patient preferences and eligibility criteria to better deliver patient-centered care.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".