Evaluating the quality of care for heart failure hospitalizations in inflammatory arthritis – A population-based cohort study
Bibliographic record
Abstract
Background: Individuals with inflammatory arthritis (IA) face an elevated risk of heart failure (HF). However, whether the quality of HF care in IA patients differs from other high-risk groups, such as those with diabetes mellitus (DM), remains unclear. Methods: This population-based cohort study in Ontario, Canada, included patients who experienced their first HF hospitalization and survived to discharge. Patients were categorized into four groups: IA alone, DM alone, IA + DM, and a general population comparator. We assessed quality care measures within 30 days of hospitalization (echocardiogram, electrocardiogram, chest x-ray) and physician follow-up within 7 days. Guideline-directed medical therapy (GDMT) adherence was evaluated within 90 days and classified as perfect, moderate, or poor. Logistic regression was used to determine whether IA was independently associated with lower HF care quality. Results: < 0.001). IA was independently linked to lower odds of moderate or perfect GDMT adherence. Conclusion: Although adherence to HF testing quality measures was high, IA patients were less likely to receive GDMT than those with DM. Further research is needed to understand the reasons for lower GDMT use in IA and its impact on HF outcomes such as re-hospitalization and mortality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".