Arthritis in Aboriginal Manitobans: evidence for a high burden of disease.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the relative burden of arthritis and patterns of care in Aboriginal Manitobans, using multiple data sets to ensure a representative picture. METHODS: Arthritis burden and healthcare utilization was ascertained using 3 separate data sources. Physician claims for 3 common ICD-9 musculoskeletal diagnoses were abstracted from the Population Health Research Data Repository for First Nations (FN) Manitobans and compared to all other Manitobans. Self-reported arthritis rates were obtained from the Manitoba First Nations Regional Longitudinal Health Survey (MFN Survey), which surveyed FN persons living on-reserve. Data on ethnicity and diagnoses were abstracted from the Arthritis Centre research database, which contains records of all patients seen at the Arthritis Centre. RESULTS: Twice as many FN Manitobans had physician claims for rheumatoid arthritis, degenerative arthritis, and unspecified arthropathy compared to all other Manitobans. MFN Survey data identified a self-reported arthritis rate of 21.0% and a rheumatoid arthritis (RA) rate of 3.0%. Data for 687 Aboriginal patients and 4135 Caucasian patients were abstracted from the Arthritis Centre database. Aboriginal patients seen in the Arthritis Centre were 2 to 4 times more likely to have a diagnosis of inflammatory disease, and less than half as likely to have noninflammatory disease. CONCLUSION: The data highlight the increased burden of arthritis in Aboriginal Manitobans, and draw attention to large gaps in our knowledge of how, why, and when Aboriginals access medical care.
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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".