Assessment of heart rate variability for prognostication of life expectancy in patients with advanced cancer
Bibliographic record
Abstract
Objectives: The goal of the study was to assess heart rate variability as a lifeexpectancy prognostication measure for patients with advanced cancer.Method: The first stage of the research was to implement a QRS detection algorithmwhich accurately discriminates normal heart beats (regular QRS complex) fromabnormal events (e.g. Premature Ventricular Contractions) from holter data filesrecorded using an orthogonal lead configuration (X, Y, and Z). Heart rate variabilitywas then assessed by calculating the standard deviation of the beat to beat intervals(SDNN = Standard deviation of N-N interval).Results: The algorithm was tested using data from the REFINE (Noninvasive RiskAssessment Early After a Myocardial Infarction) Study. 304 data files were evaluated.Normal beats were correctly classified with an accuracy of 99.9%, which correspondswell to other QRS detection algorithms. We are currently in the process of validatingour heart rate variability measures.Conclusion: We have developed a tool to assess heart rate variability. It ishypothesized that heart rate variability parameters can be used to discriminate patientswith short life expectancy (less than one week) from those with longer life expectancies.
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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 | 0.004 |
| 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".