Characteristics of subjects self-reporting arthritis in a population health survey: distinguishing between types of arthritis.
Bibliographic record
Abstract
OBJECTIVES: Arthritis is a broad term covering disparate diseases with varying prognoses. Epidemiological surveys are important tools for arthritis research, but they either fail to specify arthritis subtypes or they provide self-reported arthritis data that are potentially misclassified. This limits their use for research about arthritis subgroups. This study describes and compares characteristics of subjects self-reporting subtypes of arthritis in a Canadian epidemiological survey. We also consider the feasibility of developing methods for distinguishing subtypes of arthritis in such population surveys. METHODS: Using data from 119,904 adult participants in the Canadian Community Health Survey (CCHS) cycle 3.1, we identified those self-reporting one of four subtypes of arthritis and compared the four groups with regard to socio-demographic status, lifestyle and health characteristics, medication use, health care utilization and functional outcomes. Cross-tabulations of weighted prevalence were estimated and tested for statistical significance using the chi-square test. RESULTS: Descriptive results showed very few distinguishing characteristics across self-reported arthritis subtypes on 34 investigated variables. Participants with osteoarthritis were more likely to be older and female than other groups. Statistical testing showed no difference between rheumatoid arthritis, osteoarthritis and "other" type of arthritis for physical activity level, health conditions, medication use, health care utilization and functional limitations. DISCUSSION: Characteristics of subjects who self-report different types of arthritis in a typical population health survey (CCHS) are not sufficiently dissimilar to justify valid data analyses and interpretation by arthritis subgroup. Future studies might focus on identifying and implementing supplemental questionnaire items in epidemiological population surveys.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".