Is Diabetes Associated with Shoulder Pain or Stiffness? Results from a Population Based Study
Bibliographic record
Abstract
OBJECTIVES: To assess the association of shoulder pain and/or stiffness and diabetes mellitus in a population based cohort. METHODS: Participants were randomly recruited from the North West Adelaide Health Study, a longitudinal, population based study. In the second stage, 3128 participants were assessed for diabetes mellitus and shoulder complaints via questionnaires, the Shoulder Pain and Disability Index (SPADI), physical assessment, blood sampling for fasting plasma glucose, and HbA1c levels. RESULTS: Overall, 682 (21.8%) participants experienced shoulder pain and/or stiffness and 221 participants (7.1%) fulfilled criteria for diabetes mellitus. Those with diabetes had a higher prevalence of shoulder pain and/or stiffness (27.9% vs 21.3%; p = 0.025), and poorer SPADI disability subscore (p = 0.01) and total SPADI score (p = 0.02). After controlling for age, sex, obesity, and current smoking, the prevalence of shoulder pain and/or stiffness did not differ significantly between those with diabetes and those without (OR 1.05, 95% CI 0.76-1.45), nor were there significant differences in the SPADI disability subscore (p = 0.39) or total SPADI score (p = 0.32) between the 2 groups. After adjustment for covariates, there was no association between higher levels of HbA1c and shoulder pain and/or stiffness (p > 0.8). Range of shoulder movement was significantly reduced in those with diabetes (p < 0.05). CONCLUSIONS: There is a higher prevalence of shoulder pain and/or stiffness in people with diabetes mellitus. The differences observed between those with diabetes and those without can largely be explained by the confounding factors of age, sex, obesity, and current smoking.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".