Emergency department visits for ambulatory care sensitive conditions by persons with Rheumatoid Arthritis: A population-based study
Bibliographic record
Abstract
PURPOSE: We estimated emergency department (ED) visit rates for Canadian-indicator Ambulatory Care Sensitive Conditions (ACSCs) by persons with rheumatoid arthritis (RA) relative to age- and sex-matched general population controls. METHODS: Cases were identified using a validated definition based on International Classification of Diseases codes (years 2002-2023). We identified visits in the National Ambulatory Care Reporting System (NACRS) where the most responsible diagnosis was for any ACSC (grand mal seizures, chronic lower respiratory diseases, asthma, diabetes, heart failure and pulmonary edema, hypertension, angina) and extracted visit acuity. Annual incidence rates were calculated within five years from the index date. The incidence rate ratio between RA and non-RA was estimated using a multivariable regression model, adjusting for age, sex, location of residence, and socioeconomic status. RESULTS: RA (n = 35,770 individuals) had higher ED visit rates for all ACSCs combined compared to Non-RA (n = 94,094 individuals) (crude IRR 1.26, 95% CI 1.22, 1.31), persisting after adjusting for confounders (adjusted IRR 1.30, 95% CI 1.25, 1.34). More than two-thirds of ED visits for ACSCs were triaged as "urgent" or higher severity. Over the study period, there was a 34% increase in the proportion of ED visits for an ACSC condition among those with RA. CONCLUSION: RA cases had a 30% higher rate of avoidable ED visits in the first 5 years following diagnosis compared to non-RA. Improved ambulatory care access and care quality, inclusive of primary care and subspecialty care, is proposed to reduce the 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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".