Assessing Recovery and Establishing Prognosis Following Total Knee Arthroplasty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Information about expected rate of change after arthroplasty is critical for making prognostic decisions related to rehabilitation. The goals of this study were: (1) to describe the pattern of change in lower-extremity functional status of patients over a 1-year period after total knee arthroplasty (TKA) and (2) to describe the effect of preoperative functional status on change over time. SUBJECTS: Eighty-four patients (44 female, 40 male) with osteoarthritis, mean age of 66 years (SD=9), participated. METHODS: Repeated measurements for the Lower Extremity Functional Scale (LEFS) and the Six-Minute Walk Test (6MWT) were taken over a 1-year period. Data were plotted to examine the pattern of change over time. Different models of recovery were explored using nonlinear mixed-effects modeling that accounted for preoperative status and gender. RESULTS: Growth curves were generated that depict the rate and amount of change in LEFS scores and 6MWT distances up to 1 year following TKA. The curves account for preoperative status and gender differences across participants. DISCUSSION AND CONCLUSION: The greatest improvement occurred in the first 12 weeks after TKA. Slower improvement continued to occur from 12 weeks to 26 weeks after TKA, and little improvement occurred beyond 26 weeks after TKA. The findings can be used by physical therapists to make prognostic judgments related to the expected rate of improvement following TKA and the total amount of improvement that may be expected.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it