Bibliographic record
Abstract
OBJECTIVES: This article reports incidence rates of arthritis, based on data for people aged 40 or older who were followed over six years. The association between excess weight and arthritis, controlled for possible confounders, is also studied. DATA SOURCES: Data are from the household components of cycle 1.1 of Statistics Canada's Canadian Community Health Survey (2000/01) and from the first four cycles of the National Population Health Survey (1994/95 to 2000/01). ANALYTICAL TECHNIQUES: The prevalence of arthritis in 2000/01 was estimated using cross-sectional data; 1994/95-to-2000/01 incidence density is based on longitudinal data. Logistic regression was used to study the association between excess weight and arthritis (respondent-reported, doctor-diagnosed), while controlling for age, household income, smoking, number of physician consultations, strenuous daily activity, and other factors. MAIN RESULTS: In 2000/01, 19% of men and 31% of women aged 40 or older reported having been diagnosed with arthritis. Incidence rates of arthritis were 31 and 48 cases per 1,000 person-years for men and women, respectively. For both sexes, the odds ratio for obesity (based on self-reported height and weight) in association with subsequent arthritis was significantly elevated, at 1.6.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".