Reliability study of outcome measures in subjects with knee osteoarthritis
Bibliographic record
Abstract
Objective: The purpose of this study was to examine the reliability of the Western Ontario and McMaster Universities Osteoarthritis (WOMAC), visual analog scale (VAS), pressure pain threshold (PPT), Timed Up and Go test (TUG) and Quadriceps & Hamstrings strength in patients with knee osteoarthritis. Materials and Methods: Test-retest reliability of measurements with 1 day interval between session was determined by an Intraclass-correlation coefficient (ICC), Standard error of measurements (SEMs) and Coefficient of variation (CV) in 20 patients with knee osteoarthritis (age 63.5±9.9 years, weight 62.2±10.11 kg., height 156.6±8.58 cm.) Results: Intra tester reliability of outcome measures were good to excellent with intraclass correlation coefficient (ICC) of greater than 0.86, Standard error of measurements (SEMs) of less than 0.61% and Coefficient of variation (CV) of less than 9.38%, except the WOMAC (subpart: function) that exceeded 15%. Conclusion: These measurement outcomes were reliability and could be useful for detecting changes in management of knee osteoarthritis (OA). However, the WOMAC (sub part: function) seemed to be less reliable due to its variation. Therefore, other potential functional outcome measures should be considered to add in for detecting change in the part of function outcome. Bull Chiang Mai Assoc Med Sci 2015; 48(2): 107-114. Doi: 10.14456/jams.2015.8
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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 | 0.018 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".