Improving the Accuracy of Impedance Cardiac Output in the Intensive Care Unit: Comparison With Thermodilution Cardiac Output
Bibliographic record
Abstract
This study examined the effect of impedance algorithm adjustment to reflect abnormalities found in cardiac output estimation in the intensive care unit. Impedance (Kubicek and Sramek equations) and thermodilution were measured concurrently in 61 patients. The mean difference between Kubicek and thermodilution (n=40) was 1.47 L/min (95% confidence interval [CI], 0.47-2.47) and between Sramek and thermodilution (n=54) was 2.68 L/min (95% CI, 1.93-3.44). Exclusion of patients with valve regurgitation improved agreement between Kubicek and thermodilution (n=32), with a mean difference of 2.02 L/min (95% CI, 1.10-2.94). Multiple regression determined the role of skinfold thickness, pH, hematocrit, sodium, chloride, albumin, protein, and urea within impedance. Kubicek was recalculated using the new algorithm and recompared with thermodilution. The mean difference was -0.38 L/min (95% CI, -1.92 to 1.16). This study found poor agreement between impedance and thermodilution in critically ill patients, but exclusion of those with valve regurgitation and adjustment for hematocrit and skinfold thickness improved agreement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".