The role of vital exhaustion in predicting the recurrence of vascular events: A longitudinal study
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: The aim of this study was to examine the role of vital exhaustion in predicting the recurrence of vascular events. METHOD: = 14.7 years), 395 (48.4%) of whom reported treatment for the reoccurrence of a vascular event during the four-year follow-up period. Concurrent effects of baseline vital exhaustion (measured by a shortened version of the Maastricht Questionnaire), depression (assessed by a shortened version of the BDI), anxiety (assessed by the HADS), and hostility (assessed by a shortened version of the Cook-Medley Hostility Scale) in predicting the recurrence of T2 vascular events were examined. The analyses were also controlled for traditional risk factors, such as age, education, body mass index, smoking, alcohol use, and lack of physical activity. RESULTS: The regression analyses showed that vital exhaustion scores significantly predicted the reoccurrence of vascular events even after controlling for all covariates. None of the other psychological predictors (depression, anxiety, and hostility) was significant in the final model. CONCLUSIONS: These results suggest that despite the partial conceptual overlap with several similar constructs, vital exhaustion is a distinct phenomenon that deserves consideration when planning and implementing interventions to reduce the risk of vascular diseases.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".