How do pain and function vary with compartmental distribution and severity of radiographic knee osteoarthritis?
Bibliographic record
Abstract
OBJECTIVES: In radiographic OA (ROA) of the knee, how does radiographic severity and pattern of compartmental involvement influence symptoms? METHODS: Population-based study of 819 adults aged > or =50 yrs with knee pain. The severity of knee pain and function were measured using the Western Ontario and McMaster Universities scale. Three radiographic views of the knees were obtained. RESULTS: Seven hundred and seventy-seven participants were eligible (mean age 65.5 yrs, 357 males). Higher ROA severity in each of the tibiofemoral (TF) and patellofemoral (PF) compartments was independently associated with higher mean pain scores (TF: F(2, 700) = 9.0, P < 0.0001, PF: F(2, 700) = 12.7, P < 0.0001). The same pattern was found for mean function scores (TF: F(2, 705) = 7.1, P = 0.001, PF: F(2, 705) = 15.9, P < 0.0001). If either the TF or PF compartment was affected by moderate/severe OA, the added presence of OA in the other compartment did not increase the mean pain or function scores. CONCLUSIONS: It is the severity of radiographic disease within a compartment, rather than the distribution of radiographic disease between compartments that is associated with symptoms. ROA in the PF joint is associated with symptoms, emphasizing the importance of radiographic changes in his joint.
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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 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".