Confounding pain and function: the WOMAC's failure to accurately predict lower extremity function
Bibliographic record
Abstract
BACKGROUND: Investigations have revealed the Western Ontario and McMaster Universities Osteoarthritis Index's (WOMAC) inability to provide distinct assessments of pain and function. The Lower Extremity Functional Scale (LEFS) has not displayed this deficiency. Our purposes were to investigate further the WOMAC physical function's (WOMAC-PF) ability to accurately assess lower extremity mobility in patients undergoing total knee arthroplasty (TKA) and to establish a relationship between pre- and post-TKA WOMAC-PF and LEFS scores that accounts for the apparent bias WOMAC pain scores impose on WOMAC-PF scores. METHODS: WOMAC, LEFS, and Timed-up-and-go measures were administered before TKA and 4 days, 6 weeks, and 3 months after TKA. To evaluate the WOMAC-PF and LEFS ability to provide a distinct assessment of pain and function, a paired t-test compared pre-TKA and 4 days after TKA values. Generalized estimating equation (GEE) analysis assessed the relationship between pre- and post-TKA values: dependent variable WOMAC-PF scores; independent variables LEFS scores, and measurement occasions. RESULTS: = .61). GEE analysis revealed a linear relationship between WOMAC-PF and LEFS with similar slope coefficients for all four occasions. The relationship between WOMAC-PF and LEFS scores was virtually identical for the postarthroplasty assessment occasions. CONCLUSIONS: Our findings support previous investigations that showed the WOMAC-PF's inability to provide a valid assessment in change in function. The GEE analysis coefficients can be used to convert LEFS scores to WOMAC-PF scores that adjust for the bias between pre- and post-TKA assessments.
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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 | 0.012 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".