Determinants of Function After Total Knee Arthroplasty
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Decreasing hospital stays for patients with total knee arthroplasties (TKAs) have a direct effect on rehabilitation. The identification of modifiable determinants of postsurgical functional status would help physical therapists plan for discharge from hospitals. The purpose of this study was to identify preoperative determinants of functional status after a TKA. PARTICIPANTS: Using a community-based, prospective cohort study, data were collected from 276 patients who received a primary TKA in a Canadian health care region. Data were collected in the month before surgery and 6 months after surgery. METHODS: Function was measured using the function subscale of a disease-specific measure--the Western Ontario and McMaster Universities (WOMAC) Osteoarthritis Index--and a generic health status measure--the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36). Independent variables examined included demographic variables (eg, age, sex), medical variables (eg, diagnosis, number of comorbid conditions, ambulatory status), surgical variables (eg, type of implant, number of complications), and knee range of motion. RESULTS: At 6 months after surgery, the average WOMAC physical function score was 70.5 (SD=18.2) and the average SF-36 physical function score was 44.8 (SD=25.3). Using multiple regression analyses, baseline function, walking device, walking distance, and comorbid conditions predicted 6-month function (WOMAC: R2=.20; SF-36 physical function: R2=.27). DISCUSSION AND CONCLUSIONS: Patients who have lower preoperative function may require more intensive physical therapy intervention because they are less likely to achieve functional outcomes similar to those of patients who have less preoperative dysfunction.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".