Comparison of health‐related outcomes for arthritis, chronic joint symptoms, and sporadic joint symptoms: A population‐based study
Bibliographic record
Abstract
OBJECTIVE: To examine predictors and health outcomes for individuals reporting arthritis, chronic joint symptoms (CJS), or sporadic joint symptoms (SJS) compared to those without arthritis or joint symptoms. METHODS: Data from the 2008 Canadian Community Health Survey (n = 63,134, ages ≥15 years) were used for the analyses. Respondents not reporting arthritis as a long-term chronic health condition diagnosed by a health professional were asked about joint symptoms, excluding the back and neck, over the past 12 months and whether these symptoms were present on most days in the past month (CJS) or not (SJS). Log Poisson regression was used to estimate prevalence ratios (PRs) for reporting arthritis, CJS, and SJS, and for reporting health outcomes (physical activity, pain that limits activity, activity limitation, poor/fair self-rated health, and poor/fair self-rated mental health) and health service use (visits to primary care physicians, specialists, physiotherapists, and chiropractors, and overnight hospital stays). RESULTS: Arthritis was reported by 16.0% of the population, CJS by 10.1%, and SJS by 11.6%. Individuals with arthritis were older than those with CJS or SJS. Women reported arthritis and CJS more often. After adjusting for age, sex, socioeconomic status, lifestyle factors, and comorbidities, PRs of all outcomes were higher for the arthritis and CJS groups than the SJS group, with no significant differences in PRs for the arthritis and CJS groups, except for pain that limits activity. CONCLUSION: CJS were reported by 10% of the adult population. Similarities in outcomes to arthritis suggest that CJS have a substantial impact in the population, and that arthritis management advice is likely needed for this group.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".