Avoidable Hospitalizations in Persons With Rheumatoid Arthritis: A Population‐Based Study Using Administrative Data
Bibliographic record
Abstract
OBJECTIVE: We estimated incidence rates of avoidable hospitalizations by persons with rheumatoid arthritis (RA) relative to the general population. METHODS: We identified individuals meeting a validated case definition for RA based on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and ICD-10-CA codes in years 2002 to 2023. Four general population controls were matched to each RA case by age and sex. We identified hospitalizations for ambulatory care sensitive conditions (ACSCs), including grand mal seizures, chronic lower respiratory diseases, asthma, diabetes, heart failure and pulmonary edema, hypertension, and angina, from 2007 to 2023 by established diagnostic codes. Incidence rate ratios (IRRs) three and five years from the date of diagnosis were calculated using a multivariable regression model adjusting for age, sex, and location of residence. A Cox proportional hazards model was used to identify predictors of avoidable hospitalizations among patients with RA. RESULTS: Persons with RA (n = 83,811) had 1.12 times the risk of hospitalization for heart failure and pulmonary edema compared to those without RA (n = 190,304) (IRR 1.12, 95% confidence interval [CI] 1.01-1.25). Significant predictors of ACSC hospitalizations for RA cases were increasing age, prolonged exposure to glucocorticoids, and having comorbid conditions, especially if the comorbid condition was an ACSC (hazard ratio 10.1, 95% CI 7.8-13.0). CONCLUSION: Persons with RA are at a higher risk of potentially avoidable hospitalizations three and five years after diagnosis compared to those without RA. Improved ambulatory care access and quality, inclusive of primary care and subspecialty care, is proposed to prevent unnecessary hospitalizations and reduce burden on the acute care system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".