Knee Pain During Daily Tasks, Knee Osteoarthritis Severity, and Widespread
Bibliographic record
Abstract
Background. The presence of widespread pain is easily determined and is known to increase the risk for persistent symptoms. Objective. The study hypothesis was that people with no or minimal knee osteoarthritis (OA) and high Western Ontario and McMaster Universities (WOMAC) Pain Scale scores would be more likely than other subgroups to report widespread pain. Design. A cross-sectional design was used. Methods. Data were obtained from the Multicenter Osteoarthritis Study, which includes people with or at high risk for knee OA. The inclusion criteria were met by 755 people with unilateral knee pain and 851 people with bilateral knee pain. Widespread pain was assessed with body diagrams, and radiographic Kellgren-Lawrence grades were recorded for each knee. Knee pain during daily tasks was quantified with WOMAC Pain Scale scores. Results. Compared with people who had high levels of pain and knee OA, people with a low level of pain and a high level of knee OA, and people with low levels of pain and knee OA, a higher proportion of people with a high level of knee pain and a low level of knee OA had widespread pain. This result was particularly true for people with bilateral knee pain, for whom relative risk estimates ranged from 1.7 (95 % confidence interval1.2–2.4) to 2.3 (95 % confidence interval1.6–3.3). Limitations. The cross-sectional design was a limitation. Conclusions. People with either no or minimal knee OA and a high level of knee pain during daily tasks are particularly likely to report widespread pain. This sub-group is likely to be at risk for not responding to knee OA treatment that focuses only on physical impairments. Assessment of widespread pain along with knee pain intensity and OA status may assist physical therapists in identifying people who may require additional treatment.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".