Impact of Age on Patient-Reported Outcome Measures in Total Knee Arthroplasty
Bibliographic record
Abstract
Abstract Patient expectations and demographics are vital factors in determining patient satisfaction and outcomes from total knee arthroplasty (TKA). This study was a retrospective chart review that analyzed data from TKA patients to determine the impact of age on patient-reported outcomes measures following TKA. Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Oxford knee scores were collected as primary outcome measures from 356 consecutive patients who underwent TKA. Oxford knee scores were further divided into pain and function subscores. Patients were age categorized as <50, 50 to 59, 60 to 69, 70 to 79, and >79. Preoperative scores were compared among age categories including age category, gender, body mass index (BMI), and length of stay (LOS) in the model as fixed effects. Scores collected postoperatively (∼10, 30, 90, and 180 days postoperation) were analyzed as repeated measures including age category, day and their interaction, gender, BMI, LOS, and preoperative score in the model. Preoperative OXFORD scores significantly differed among age categories (p < 0.05) and were numerically higher for the older (≥60 years old) compared with younger patients (<60 years old). After adjusting for preoperative scores, postoperative WOMAC and overall, pain, and function OXFORD scores significantly differed among the age groups (p < 0.05), with patients younger than 60 years reporting the worst scores in the postoperative time period. Older patients reported better preoperative overall, pain, and function scores and greater post-TKA outcomes than younger patients. A better understanding of factors that influence patient-reported outcomes can help providers to better manage patient expectations.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".