Negative affectivity and social inhibition are associated with increased cardiac readmission in patients with heart failure: A preliminary observation study
Bibliographic record
Abstract
BACKGROUND: Type D personality was hypothesized to influence clinical and patient-centered outcomes patients with heart failure. The aim of this study was to investigate the association between negative affectivity and social inhibition components of Type D personality and cardiac readmission in patients with heart failure. METHODS: A prospective observational study design was used. A total of 222 patients with heart failure were recruited from the department of cardiology in two regional hospitals in Taiwan. The 14-item Type D Scale-Taiwanese version was used to assess negative affectivity and social inhibition of the patients. Logistic regression analyses were conducted to determine the association of both Z-score transformed and dichotomized negative affectivity and social inhibition with 6-month and 18-month cardiac readmissions. RESULTS: A total of 55 patients (24.8%) and 89 patients (40.1%) had cardiac readmissions within 6 months and 18 months, respectively. Multiple logistic regression analyses of Z-score transformed negative affectivity and social inhibition were significantly associated with (1) 6-month cardiac readmission with odds ratios of 1.62 (P = 0.003) and 1.48 (P = 0.014), respectively and (2) 18-month cardiac readmission with odds ratios of 1.45 (P = 0.013) and 1.38 (P = 0.031), respectively. Similar findings were obtained when negative affectivity and social inhibition were analyzed as dichotomized scores. CONCLUSIONS: Negative affectivity and social inhibition components of the Type D personality were significantly associated with a higher risk of cardiac readmission in both 6 months and 18 months after the initial hospitalization in patients with heart failure.
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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 | 0.000 | 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 | 0.001 |
| 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".