Age, Sex, Body Mass Index, Education, and Social Support Influence Functional Results After Total Knee Arthroplasty
Bibliographic record
Abstract
INTRODUCTION: Total knee arthroplasty (TKA) is an effective treatment for knee osteoarthritis. Patient-reported outcome after TKA is influenced by multiple patient-related factors. The aim of this study was to prospectively evaluate preoperative patient-related factors and to compare the self-reported outcomes 1 year after TKA among groups differing by age, sex, body mass index (BMI), education, and social support level. METHODS: 314 patients, who underwent TKA in Vilnius Republican University Hospital between the end of 2012 and the middle of 2014, were included in a study. The preoperative and 12-month follow-up measurements were obtained using Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Short Form-12 (SF-12). Differences between patient groups according to gender, age, BMI, level of education, and level of social support were analyzed. RESULTS: = .008). Better preoperative WOMAC and SF-12 scores were a predictor of better outcome 1 year after surgery. There was no difference in postoperative scores in different age, BMI, and education groups according to WOMAC and SF-12. CONCLUSION: There is no difference in self-reported functional outcome between patient groups differing in age, BMI, and education. Men and socially supported patients demonstrate better postoperative functional results 12 months after TKA. Better preoperative knee function and overall physical and mental function are predictors of better outcome 1 year after TKA. Age and obesity should not be limiting factors when considering who should receive this surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".