Association of Intermittent and Constant Knee Pain Patterns With Knee Pain Severity and With Radiographic Knee Osteoarthritis Duration and Severity
Bibliographic record
Abstract
OBJECTIVE: To examine the relation of knee pain patterns to pain severity and to radiographic osteoarthritis (OA) severity and duration. METHODS: The Multicenter Osteoarthritis Study is a longitudinal cohort of older adults with or at risk of knee OA. Participants' Intermittent and Constant Osteoarthritis Pain (ICOAP) scores were characterized as 1) no intermittent or constant pain, 2) intermittent pain only, 3) constant pain only, and 4) a combination of constant and intermittent pain. Knee pain severity was assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale and a visual analog scale (VAS). Radiographic knee OA (ROA) severity was defined as Kellgren/Lawrence grade ≥2, and ROA duration was defined according to the clinic visit at which ROA was first noted. We assessed the relation of ICOAP pain patterns to knee pain severity, ROA severity, and ROA duration using regression models with generalized estimating equations. RESULTS: , 60% female). Higher ICOAP pain patterns, i.e., a mix of constant and intermittent pain, were associated with greater WOMAC pain severity compared with those patients without either pain pattern (odds ratio [OR] 43.2 [95% confidence interval (95% CI) 26.4-61.3]). Results were similar for the VAS (OR 71.2 [95% CI 45.7-110.9]). Those patients with more severe and longer duration of ROA were more likely to have a mix of constant and intermittent pain compared with those without either pain (OR 3.7 [95% CI 3.1-4.6] and OR 2.9 [95% CI 2.5-3.5], respectively). CONCLUSION: Knee pain patterns are associated with radiographic disease stage and duration, as well as pain severity, highlighting the fact that pain patterns are important for understanding symptomatic disease progression.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".