Novel method to quantify loss of heart rate variability in pediatric multiple organ failure*
Bibliographic record
Abstract
OBJECTIVE: To develop a power-law model for measurement of heart rate variability (HRV) and to compare this model with established methods for measuring HRV in a group of children with organ failure (OF). DESIGN: Prospective, observational study. SETTING: Pediatric intensive care unit of a tertiary children's hospital. PATIENTS: A total of 104 measurements were made on 50 patients (median age, 8 months; range, 2 days to 16 yrs) and categorized into three groups according to the number of simultaneous organs failing: 0-1 OF, 2 OF, and >/=3 OF. INTERVENTIONS: Heart rate was recorded over a 5-min period when patients were hemodynamically stable. The power-law model represents a power function relating frequency distribution to magnitude of effect (in this case, squared deviation from the mean heart rate). Plotting the data on a bi-logarithmic scale produces a regression line for each measurement, described in terms of r2, slope, and x-intercept. Comparison with other HRV measures included two time-domain measures (sd of the normal R-R intervals and the square root of the mean squared differences of successive normal R-R intervals), one frequency-domain method (power spectral analysis), and one nonlinear method (detrended fluctuation analysis). MEASUREMENTS AND RESULTS: For the power-law model, patients exhibited a similar r2 of.87 (.09) (mean [sd]) and slope of -1.80 (0.29), regardless of the degree of OF. HRV could thus be described purely in terms of x-intercept, which demonstrated a left shift with increasing OF (p <.001). This was independent of age and heart rate. Loss of HRV with increasing OF was demonstrated by all methods; however, only the power-law model was able to discriminate between each OF group. Using the model, change in HRV in individual patients over successive days often concurred qualitatively with the change in OF status. CONCLUSION: The power-law model is an appropriate measure of HRV in pediatric patients, being neither age nor heart rate sensitive. Loss of HRV occurs with increasing OF; this effect was better demonstrated by the model compared with other measures of HRV.
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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 | 0.005 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".