Associations Between Biomarkers of Joint Metabolism, Hand Osteoarthritis, and Hand Pain and Function: The Johnston County Osteoarthritis Project
Bibliographic record
Abstract
OBJECTIVE: To determine the associations between joint metabolism biomarkers and hand radiographic osteoarthritis [(rOA), based on Kellgren Lawrence (KL) grade ≥ 2], symptoms, and function. METHODS: Cross-sectional data were available for 663 participants (mean age 63 yrs, 63% white, 49% women). Three definitions of hand rOA were considered: (1) a composite measure involving at least 3 hand joints distributed bilaterally with 2 of 3 in the same joint group, including ≥ 1 distal interphalangeal joint, without metacarpophalangeal (MCP) swelling; (2) rOA in at least 1 joint of a group; and (3) number of joints with KL ≥ 2. We assessed hand symptoms and the 15-item Australian Canadian Hand Osteoarthritis Index (AUSCAN; Likert format). We measured serum cartilage oligomeric matrix protein (sCOMP), hyaluronic acid (sHA), carboxy-terminal propeptide of type II collagen, type II collagen degradation product, urinary C-terminal crosslinked telopeptide of type II collagen, and urinary N-terminal crosslinked telopeptide. Linear regression models were performed to assess associations between each biomarker with hand rOA, AUSCAN, and symptoms, adjusting for age, sex, race, current smoking/drinking status, body mass index, and hip and knee rOA. RESULTS: In adjusted analyses, MCP (p < 0.0001) and carpometacarpal rOA (p = 0.003), and a higher number of hand joints with rOA (p = 0.009), were associated with higher levels of sHA. Positive associations were seen between AUSCAN and hand symptoms and levels of sCOMP (p ≤ 0.003) and sHA (p ≤ 0.048). CONCLUSION: Hand symptoms and higher AUSCAN scores were independently associated with higher levels of both sCOMP and sHA; hand rOA was associated only with sHA levels.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".