A Longitudinal Study of Ambulatory Physician Encounters, Emergency Room Visits, and Hospitalizations by Patients with Rheumatoid Arthritis: A 13-year Population Health Study
Bibliographic record
Abstract
OBJECTIVE: To determine total physician encounters, emergency room (ER) visits, and hospitalizations in an incident cohort of rheumatoid arthritis (RA) cases and matched control patients over 13 years. METHODS: A retrospective cohort study was performed using administrative healthcare data from about 1 million people with access to universal healthcare. Using the International Classification of Diseases, 9th ed (ICD-9) and ICD-10 diagnostic codes, 7 RA case definitions were used. Each case was matched by age and sex to 4 randomly selected controls. Data included physician billings, ER visits, and hospital discharges over 13 years. RESULTS: The number of incident RA cases varied from 3497 to 27,694, depending on the case definition. The mean age varied from 54.3 to 65.0 years, and the proportion of women from 67.8% to 71.3%. The number of physician encounters by patients with RA was significantly higher than by controls. It was highest in the index year and declined promptly thereafter for all case definitions and by 12.2%-46.8% after 10 years. Encounters with subspecialty physicians fell by 61% (rheumatologists) and 34% (internal medicine). In contrast, clinical encounters with family physicians and other physicians fell by only 9%. Visits to the ER and hospital admissions were also significantly higher in RA cases, particularly early in the disease, and fell significantly over the followup. CONCLUSION: In patients with RA, healthcare use is highest in the first year following the diagnosis, which is also the time of maximal involvement by rheumatologists. Use declines over time, and encounters with patients' family physicians predominate over other physician groups.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".