[Patient factors influencing preoperative expectations of patients undergoing total knee arthroplasty].
Bibliographic record
Abstract
OBJECTIVE: To investigate the expectations of patients for total knee arthroplasty (TKA), and to analyze its influencing factors. METHODS: Experimental design: Single center, retrospective, multiple regression analysis. The data including the age, height, and weight of 108 patients undergoing unilateral TKA due to end-stage osteoarthritis were obtained. The patients' preoperative Hospital for Special Surgery (HSS) knee arthroplasty expectation score, the Western Ontario and McMaster Universities (WOMAC) score, Knee Society score (KSS), the MOS 36-item short-from health survey (SF-36) score, and visual analogue scale (VAS) were evaluated, and the 30-second chair-stand test (30-CST), 40-meter fast-paced walk test (40-FPWT), 12-level stair-climb test (12-SCT), 3-meter timed up-and-go test (TUG), 6-minute walk test (6-MWT), and recorded daily steps for 7 consecutive days were performed. The SPSS 22.0 software was used for statistical analysis. The observed values of various data were described. Pearson correlation analysis was used to evaluate the correlation between various parameters, and the multi-factor linear regression analysis was used to investigate the influencing factors of the patients preoperative expectation scores. RESULTS: < 0.05). CONCLUSION: The estimated expectation score of patients before TKA is not high. Patients with more severe preoperative pain, worse physical function, and lower overall health are more eager to improve after surgery. Thus surgeons must communicate fully with patients with unrealistic expectations before surgery in order to obtain more satisfactory results postoperatively.
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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 | 0.000 | 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".