Efficacy of the Reactive Oxygen Metabolite Test as a Predictor of Initial Heart Failure Hospitalization in Elderly Patients With Chronic Heart Failure
Bibliographic record
Abstract
BACKGROUND: and used in clinical settings. Conversely, data regarding the utility of the d-ROMs test as a predictor of patients with chronic heart failure (CHF) are limited. This prospective study aims to elucidate the efficacy of the d-ROMs test as a predictor of initial heart failure (HF) hospitalization in elderly patients with CHF. METHODS: A total of 428 elderly outpatients with CHF with no history of HF hospitalization (108 males, 320 females; mean age, 75 ± 7 years) were enrolled. Based on the median value of d-ROMs test levels (303 U.CARR), the patients were divided into the following two groups: group L (low d-ROMs test levels) and group H (high d-ROMs test levels). The utility of the d-ROMs test as a predictor of initial HF hospitalization was evaluated. RESULTS: During the 88.1-month follow-up period, 58 HF cases were hospitalized (group L, 17 cases; group H, 41 cases; P < 0.001, log-rank test). Multivariate Cox regression analyses revealed that group H exhibited a significantly higher risk for HF hospitalization than did group L (hazard ratio (HR), 2.35; 95% confidence interval (CI), 1.37 - 4.43; P < 0.01). Furthermore, the HR (vs. group L with low brain natriuretic peptide (BNP) levels (< 200 pg/mL), HR, 9.18; 95% CI, 4.78 - 22.94; P < 0.001) for the incidence of HF hospitalization increased in group H with high BNP levels (≥ 200 pg/mL). CONCLUSIONS: The present study demonstrates that high d-ROMs test levels predict initial HF hospitalization in elderly patients with CHF. In addition, the predictive value for the incidence of HF hospitalization increases by using a combination of two biomarkers as d-ROMs test and BNP levels.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".