Knee Pain Patterns and Associations with Pain and Function in Persons with or at Risk for Symptomatic Radiographic Osteoarthritis: A Cross-sectional Analysis
Bibliographic record
Abstract
OBJECTIVE: Knee pain location is routinely assessed in clinical practice. We determined the patterns of patient-reported pain locations for persons with knee osteoarthritis (OA). We also examined associations between knee pain patterns and severity of self-reported pain with activity and self-reported functional status. METHODS: The Osteoarthritis Initiative data were used to examine reports of pain location (localized, regional, or global) and type and extent of knee OA. Multivariable ANCOVA models were used to determine associations between the Knee Injury and Osteoarthritis Outcome Survey (KOOS) Pain and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) Function scales and pain location after adjusting for potential confounding. We also used radar graphs to illustrate pain patterns for various locations and severity of knee OA. RESULTS: Radar graphs of 2696 knees indicated that pain pattern and location and extent of knee OA demonstrate substantial overlap. An interaction between race and pain location was found for WOMAC Function, but not for KOOS Pain scores. Global knee pain was associated (p < 0.001) with substantially worse function (by 6.5 points in African Americans) compared with pain that was localized. Knee pain reported as global was independently associated (p < 0.001) with clinically important lower (worse by 3.9 points) KOOS Pain scores compared with pain that was localized. CONCLUSION: Pain patterns are not useful for inferring potential location or severity of knee OA in individual patients, but knee pain patterns that are global are independently associated with worse pain and function compared with localized pain, and associations differ for function based on race.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".