Physical performance tests, self-reported outcomes, and accidental falls before and after total knee arthroplasty: An exploratory study
Bibliographic record
Abstract
This longitudinal, observational study explored the relationship between physical performance tests, self-reported outcomes, and accidental falling, before and after total knee arthroplasty (TKA). Thirty-seven patients were randomly selected from a larger study of falling before and after surgery conducted at a UK National Health Service Orthopaedic Unit. Physical performance tests were the Berg Balance Score (BBS), Timed Up and Go (TUG), and Hand Grip Strength (HGS). Self-reported outcomes incorporated the Western Ontario and McMaster's Osteoarthritis Index (WOMAC), Activities Balance Confidence Scale (ABC-UK), Geriatric Depression Scale (GDS), and accidental falls. Paired pre- and postoperative data were available on 22 patients. A total of 22.7% patients fell before and after TKA. Postoperative improvement in BBS and TUG was found in 41% and 50% of patients, respectively, HGS did not change. BBS showed a consistent moderate-to-strong association with other physical tests both before and after surgery; TUG (rs -0.76; rs -0.90), maximal HGS (r 0.49; r 0.48), and self-report measures; ABC-UK (r 0.52; r 0.74), WOMAC stiffness (r -0.53; r -0.48), and WOMAC function (r -0.56; r -0.45). Although self-report questionnaires are an efficient, cost-effective approach to outcome assessment in TKA, there is a growing case for inclusion of physical performance tests. The Berg Balance Score may be a useful addition to outcome assessment in patients with TKA.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".