Auto-Entrainment Risk Assessment in Heart Failure
Bibliographic record
Abstract
BACKGROUND: Risk assessment studies use a suite of nominally independent noninvasive heart rate metrics, often brought together in a statistical model to compute a risk score. The ongoing need to noninvasively identify the higher risk patients requiring more invasive investigations/interventions drives the search for better noninvasive predictive metrics, with increased sensitivity. Many varieties of autoregulatory malfunction occur within the cardiovascular system; thus, it seems a daunting challenge to build predictive models that account for all potential modes of failure. Auto-entrainment (AE) methodology was developed to help address this challenge. METHODS AND RESULTS: AE methodology tests intrinsic capacity to maintain a stable and coherent oscillatory dynamic of autoregulatory control via respiratory entrainment of the blood pressure and heart period. Using cardiovascular death (n=18) at follow-up (1.5 years) as the end point, analysis of AE measurements from 148 patients with heart failure revealed 2 parameters significantly predictive of death. Using logistic regression, the magnitude of systolic pulsus alternans measured during AE had predictive sensitivity of 90% (confidence interval, 62%-100% and specificity of 62% (confidence interval, 49%-74%). The capacity to maintain a stable oscillatory dynamic was measured by the fraction of the total RR-interval spectral power contained within the AE-band. This capacity had predictive sensitivity of 73% (confidence interval, 47%-99%) and specificity of 55% (confidence interval, 43%-66%). CONCLUSIONS: AE methodology provides a noninvasive platform to assess the integrity of cardiovascular autoregulatory control systems for risk assessment in heart failure patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".