Dual Healthcare System Use is Associated with Higher Mortality in Heart Failure
Bibliographic record
Abstract
BACKGROUND: Heart failure is associated with high mortality, and health system-related factors contribute to this risk. Dual health system use occurs when patients receive care from multiple facilities over time, and such fractured care has been associated with higher healthcare utilization and higher mortality in selected conditions.METHODS: We analyzed a cohort of 13,948 U.S. Veterans receiving emergency department (ED) or hospital care for heart failure between 2007-2011 using information from the VA, Medicare, and an all-payor state-level claims database. Cox proportional hazards regression was used to model the association between all-cause mortality and dual use comparing dual users to those receiving VA-only care or non-VA only care.RESULTS: In fully adjusted models accounting for age, gender, race/ethnicity, marital status, disability, and comorbidities, dual use Veterans with heart failure had higher hazard for mortality from their date of entry into the cohort (HR 1.21, 95% CI 1.11, 1.32, p<0.0001) and from the date of their last hospitalization (HR 1.40, 95% CI 1.28-1.53, p<0.0001) as compared to VA-only users. Non-VA only users did not have significantly different hazard for mortality compared to VA-only users. Additional models in a subset of patients which also included laboratory data for brain-type natriuretic peptide, blood urea nitrogen, and serum sodium yielded similar results.CONCLUSIONS: Dual use appears to be associated with higher risk for mortality among Veterans with heart failure. While cross-system care is necessary and even desirable in many situations, strategies to identify high-risk patients and to mitigate risks of fractured care are warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".