Higher Vagal Activity as Related to Survival in Patients With Advanced Breast Cancer
Bibliographic record
Abstract
OBJECTIVE: High levels of high-frequency heart rate variability (HF-HRV), related to parasympathetic-nervous-system functioning, have been associated with longer survival in patients with myocardial infarction and acute trauma and in patients undergoing palliative care. From animal studies linking higher vagal activity with better immune system functioning and reduced metastases, we hypothesized that higher HF-HRV would predict longer survival in patients with metastatic or recurrent breast cancer (MRBC). METHODS: Eighty-seven patients with MRBC participated in a laboratory task including a 5-minute resting baseline electrocardiogram. HF-HRV was computed as the natural logarithm of the summed power spectral density of R-R intervals (0.15-0.50 Hz). In this secondary analysis of a study testing whether diurnal cortisol slope predicted survival, we tested the association between resting baseline HF-HRV on survival using Cox proportional hazards models. RESULTS: A total of 50 patients died during a median follow-up of 7.99 years. Higher baseline HF-HRV predicted significantly longer survival, with a hazard ratio of 0.75 (95% confidence interval = 0.60-0.92, p = .006). Visceral metastasis status and baseline heart rate were related to both HF-HRV and survival. However, a combination of HF-HRV and heart rate further improved survival prediction, with a hazard ratio of 0.64 (95% confidence interval = 0.48-0.85, p = .002). CONCLUSIONS: Vagal activity of patients with MRBC strongly predicted their survival, extending the known predictive window of HF-HRV in cancer beyond palliative care. Vagal activity can be altered by behavioral, pharmacological, and surgical interventions and may be a promising target for extending life expectancy in patients with metastasizing cancer.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".