Socio-Economic Status and the Effect of Guideline-Directed Medical Therapy in the STRONG-HF Study
Bibliographic record
Abstract
AIMS: Acute heart failure (AHF) impacts millions globally, with outcomes varying based on socio-economic status (SES). METHODS: SES measured by annual household income, years of education and medical insurance coverage. Each patient's income and education level relative to the median or mean, respectively, in the country was calculated, and categorized into tertiles (0, 1 or 2 from lowest to highest). SES scores (0-5) were computed as the sum of these levels plus insurance coverage (0 = no or 1 = yes). Patients' baseline characteristics, outcomes (HF readmission, death and their composite) and the effect of high-intensity care (HIC) vs. usual care (UC) were examined by SES scores 0-2, 3 and 4-5. RESULTS: Lower SES patients, who were younger, predominantly female, Black and non-European, had fewer comorbidities such as atrial fibrillation, diabetes and ischaemic heart disease and exhibited milder HF, indicated by a lower NYHA class, lower creatinine and higher cholesterol before discharge. Despite having milder HF and less comorbidities, after adjusting for baseline characteristics, patients with higher SES had numerically better outcomes, though differences were not statistically significant. 180-day hazard ratios (HRs) for HF readmission or death were 0.75 (95% CI 0.48-1.16) for SES scores of 3 and 0.85 (95% CI 0.58-1.23) for scores of 4-5, compared to 0-2. Higher SES patients had numerically better treatment effect from HIC, with HRs of 0.69 for SES 0-2, 0.72 for SES 3 and 0.50 for SES 4-5. CONCLUSIONS: In this post hoc analysis of the STRONG-HF study, lower SES was associated with milder acute HF but similar 180-day outcomes. Higher SES patients benefitted more from HIC.
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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) | 0.001 | 0.002 |
| 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) | 0.002 | 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".