Hemodynamic monitoring by transpulmonary thermodilution and pulse contour analysis in critically ill children
Bibliographic record
Abstract
OBJECTIVES: To summarize the physiologic principles underlying the hemodynamic monitoring using the PiCCO device (Pulsion, Munich, Germany) incorporating the transpulmonary thermodilution technique, the pulse contour cardiac output, and estimation of the arterial pressure variation method. Analysis and review of the current literature. DESIGN: A MEDLINE-based literature search using the key words transpulmonary thermodilution, pulse contour analysis, cardiac output, animal models, and child. MEASUREMENTS AND MAIN RESULTS: The bias and precision of cardiac output measured by transpulmonary thermodilution are reliable. The reproducibility for repeated measurements is approximately 5% and the percentage error is approximately 15%. Transpulmonary thermodilution may adequately track changes in cardiac output in animals submitted to hypovolemic conditions and during volume loading. Conversely, data from experimental and clinical studies suggest that continuous monitoring of cardiac output using pulse contour analysis requires careful interpretation because periodic recalibration with transpulmonary thermodilution is necessary. Transpulmonary thermodilution-derived static indicator of cardiac preload (global end-diastolic volume, intrathoracic blood volume) may be more sensitive than conventional measurements of vascular filling pressure. However, the value of stroke volume variation or pulse pressure variation have not been evaluated in pediatric patients. Further studies are needed to determine whether theoretical assumptions underlying the measurement of extravascular lung water are valid in children. CONCLUSIONS: The PiCCO device may be a useful adjunct for hemodynamic monitoring in critically ill children. Further studies are needed to clarify the reliability and clinical value of pulse contour method and extravascular lung water measurement.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".