Association Between Symptomatic Knee Osteoarthritis and Target Glycemic Control in Individuals With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: Knee osteoarthritis (OA) commonly affects individuals with type 2 diabetes (T2DM) and is associated with increased risk of diabetes-related complications. To better understand potential mechanisms, we examined the association between symptomatic knee OA and glycemic control in individuals with T2DM. METHODS: In this cross-sectional study, we recruited individuals with T2DM aged ≥45 years from three academic centers in Canada. Online questionnaires assessed demographics, medical history, and joint symptoms. We abstracted glycosylated hemoglobin (HbA1c) from clinic records. Knee OA was defined as fulfilling the National Institute of Clinical Excellence criteria. Target glycemic control was defined as an HbA1c level ≤7.0%. Multivariable logistic regression assessed the association between knee OA and target glycemic control, adjusting for age, gender, education level, and body mass index. Secondary analyses assessed associations between knee OA with pain ≥20/100 (and knee OA with walking difficulty) and target glycemic control. RESULTS: Among 351 participants (mean age 66.2 years, 50.7% women), 28.5% met the criteria for knee OA and 43.9% were at glycemic target. In unadjusted analyses, those with knee OA had lower odds of being at target glycemic control (odds ratio [OR] 0.60, 95% confidence interval [CI] 0.37-0.97), but the association was not statistically significant after adjusting for confounders (OR 0.65, 95% CI 0.39-1.08). In those with knee OA with pain ≥20/100, a negative association with target glycemic control was statistically significant in adjusted analysis (OR 0.58, 95% CI 0.34-0.99). CONCLUSION: Individuals with T2DM and painful knee OA are less likely to be at glycemic target, increasing their risk of diabetes complications.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".