Comparison of cardiac output determined by arterial pulse pressure waveform analysis method (FloTrac/Vigileo) versus lithium dilution method in anesthetized dogs
Bibliographic record
Abstract
OBJECTIVE: To compare the determination of cardiac output (CO) via arterial pulse pressure waveform analysis (FloTrac/Vigileo) versus lithium dilution method. DESIGN: Prospective study. SETTING: University teaching hospital. ANIMALS: Six adult dogs. INTERVENTIONS: Dogs were instrumented for CO determinations using lithium dilution (LiDCO) and FloTrac/Vigileo methods. Direct blood pressure, heart rate, arterial blood gases, and end-tidal isoflurane (ETIso) and CO(2) concentrations were measured throughout the study while CO was manipulated with different depth of anesthesia and rapid administration of isotonic crystalloids at 60 mL/kg/h. MEASUREMENTS AND MAIN RESULTS: Baseline CO measurements were obtained at 1.3% ETIso and were lowered by 3% ETIso. Measurements were obtained in duplicate or triplicate with LiDCO and averaged for comparison with corresponding values measured continuously with the FloTrac/Vigileo method. For 30 comparisons between methods, a mean bias of -100 mL/kg/min and 95% limits of agreement between -311 and +112 mL/kg/min (212 mL/kg/min) was determined. The mean (mL/kg/min) of the differences of LiDCO-Vigileo=62.0402+-0.8383 × Vigileo, and the correlation coefficient (r) between the 2 methods 0.70 for all CO determinations. The repeatability coefficients for the individual LiDCO and FloTrac/Vigileo methods were 187 and 400 mL/kg/min, respectively. Mean LiDCO and FloTrac/Vigileo values from all measurements were 145 ± 68 mL/kg/min (range, 64-354) and 244 ± 144 mL/kg/min (range, 89-624), respectively. The overall mean relative error was 48 ± 14%. CONCLUSION: The FloTrac/Vigileo overestimated CO values compared with LiDCO and the relative error was high, which makes this method unreliable for use in dogs.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".