Consistency of Knee Pain and Risk of Knee Replacement: The Multicenter Osteoarthritis Study
Bibliographic record
Abstract
OBJECTIVE: To examine whether the consistency or persistence of knee pain, in addition to its severity, predicts incident total knee replacement (TKR). METHODS: The Multicenter Osteoarthritis Study (MOST) is a longitudinal study of persons aged 50 to 79 years with symptomatic knee osteoarthritis or at high risk of disease. Subjects were queried about the presence of knee pain on most days of the previous 30 days (i.e., frequent knee pain; FKP) at 2 timepoints: a telephone screen followed by a clinic visit (median separation 4 weeks). We defined a knee as having "consistent pain" if the subject answered positively to the FKP question at both timepoints, "inconsistent pain" if FKP was positive at only one timepoint, or as "no FKP" if negative at both. We examined the association between consistent FKP and risk of TKR using multiple binomial regression with generalized estimating equations. RESULTS: In 3026 persons (mean age 63 yrs, mean body mass index 30.4), 2979 knees (50%) had no FKP at baseline, 1279 knees (21.5%) had inconsistent FKP, and 1696 knees (28.5%) had consistent FKP. Risk of TKR over 30 months was 0.8%, 2.6%, and 8.8% for knees with no, inconsistent, and consistent FKP, respectively. Relative risks of TKR over 30 months were 1.2 (95% CI 0.6-2.3) and 2.3 (95% CI 1.2-4.4) for knees with inconsistent and consistent FKP, compared with those without FKP. This association was consistent across each level of pain severity on the Western Ontario and McMaster Universities Osteoarthritis Index. CONCLUSION: Consistency of frequent knee pain is associated with an increased risk of TKR independently of knee pain severity.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".