Assessing the Patient-Specific Functional Scale's Ability to Detect Early Recovery Following Total Knee Arthroplasty
Bibliographic record
Abstract
BACKGROUND: The Patient-Specific Functional Scale (PSFS) has received considerable attention over the last 2 decades; however, validation studies have not examined its performance in patients after total knee arthroplasty (TKA). OBJECTIVE: The purpose of this study was to investigate the ability of the PSFS to detect change in patients post-TKA by comparing PSFS change scores with Lower Extremity Functional Scale (LEFS) and pooled impairment change scores. METHODS: One hundred thirty-three patients participating in a post-TKA exercise class were assessed at their initial and discharge visits. Initial assessments occurred within 28 days of arthroplasty; follow-up assessments occurred within 80 days of surgery. At both assessments, participants completed the PSFS, LEFS, and the P4 pain measure, and their knee range of motion (ROM) and extensor strength were measured. The ability to detect change was expressed as the standardized response mean (SRM) and as a correlation between the PSFS change scores and 2 reference standards: (1) LEFS change scores and (2) pooled impairment change scores. The pooled impairment measure consisted of pain, ROM, and strength change scores. RESULTS: The SRMs were PSFS 4.60 (95% confidence interval [CI]=4.00, 5.36) for the PSFS and 2.28 (95% CI=2.04, 2.60) for the LEFS. The correlation between the PSFS and pooled impairment change scores was 0.12 (95% CI=-0.04, 0.25), and the correlation between the PSFS and LEFS changes scores was 0.18 (0.02, 0.34). LIMITATIONS: The order of measure administration was not standardized, and fixed activity set does not reflect clinical application in many instances. CONCLUSIONS: The results suggest that the PSFS is adept at detecting improvement in patients post-TKA but that the PSFS, like other patient-specific measures, is likely to be of limited value in distinguishing different levels of change among patients.
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 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 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".