Symptom Assessment in Knee Osteoarthritis Needs to Account for Physical Activity Level
Bibliographic record
Abstract
OBJECTIVE: Pain is not always correlated with severity of radiographic osteoarthritis (OA), possibly because people modify activities to manage symptoms. Measures of symptoms that consider pain in the context of activity level may therefore provide greater discrimination than a measure of pain alone. We undertook this study to compare discrimination provided by a measure of pain alone with that provided by combined measures of pain in the context of physical activity across radiographic OA severity levels. METHODS: This was a cross-sectional study nested within the Osteoarthritis Initiative (OAI). The population was drawn from 2,127 persons enrolled in an OAI accelerometer monitoring substudy, including those with and those without knee OA. Two composite pain and activity knee symptom (PAKS) scores were calculated as the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score (plus 1) divided by a physical activity measure (step count for the first PAKS score [PAKS1 score] and activity count for the second PAKS score [PAKS2 score]). Symptom score discrimination across Kellgren/Lawrence (K/L) grades was evaluated using histograms and quantile regression. RESULTS: A total of 1,806 participants (55.5% of whom were women) were included (mean ± SD age 65.1 ± 9.1 years, mean ± SD body mass index 28.4 ± 4.8 kg/m(2) ). The WOMAC pain score, but not the PAKS scores, exhibited a floor effect. The adjusted median WOMAC pain scores by K/L grades 0-4 were 0, 0, 0, 1, and 3, respectively. The adjusted median PAKS1 scores were 24.9, 26.0, 32.4, 46.1, and 97.9, respectively, and the adjusted median PAKS2 scores were 7.2, 7.2, 9.2, 12.9, and 23.8, respectively. The PAKS scores had more statistically significant comparisons between K/L grades than did the WOMAC pain score. CONCLUSION: Symptom assessments incorporating pain and physical activity did not exhibit a floor effect and were better able to discriminate radiographic severity than an assessment of pain alone, particularly in milder disease. Pain in the context of physical activity level should be used to assess knee OA symptoms.
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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 | 0.010 | 0.033 |
| 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".