I don’t know what type of arthritis I have: A population-based comparison of people with arthritis who knew their specific type and those who didn’t
Bibliographic record
Abstract
OBJECTIVE: To understand differences between people with arthritis who do not know their type (DK) compared to those reporting osteoarthritis (OA) or inflammatory and autoimmune types of arthritis (IAA), including the receipt of appropriate health care, information, and services. METHODS: Analysis of the Survey on Living with Chronic Disease in Canada-Arthritis Component. Respondents aged ≥20 years with health professional-diagnosed arthritis (n = 4,385) were characterized as reporting DK, OA or IAA. Variables: arthritis characteristics (duration, number and site of joints affected), arthritis impact (current pain and fatigue, difficulty in sleeping and daily activities, impact on life), health (self-rated general and mental health, life stress), arthritis management strategies (seeing health professionals, medication use, assistive devices, receipt of arthritis information, self-management activities). Multinomial logistic and log-Poisson regressions were used, as appropriate, to compare the DK to the OA and IAA groups. RESULTS: In this arthritis sample, 44.2% were in the DK group, 38.3% reported OA and 17.5% reported IAA. Those in the DK group were more likely to be younger, have low income, low education, and be of non-white cultural background compared to those with OA. There were no significant differences in arthritis impact, but the DK group was less likely to have received information on, or have used, arthritis management strategies. CONCLUSIONS: The sociodemographic characteristics of the DK group suggest they likely have lower health literacy. They were less likely to have accessed health care and other support services, indicating this is an important group for health education, both for individuals with arthritis and health care providers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".