Correlation of carotid blood flow and corrected carotid flow time with invasive cardiac output measurements
Bibliographic record
Abstract
Non-invasive measures that can accurately estimate cardiac output may help identify volume-responsive patients. This study seeks to compare two non-invasive measures (corrected carotid flow time and carotid blood flow) and their correlations with invasive reference measurements of cardiac output. Consenting adult patients ( n = 51) at Massachusetts General Hospital cardiac catheterization laboratory undergoing right heart catheterization between February and April 2016 were included. Carotid ultrasound images were obtained concurrently with cardiac output measurements, obtained by the thermodilution method in the absence of severe tricuspid regurgitation and by the Fick oxygen method otherwise. Corrected carotid flow time was calculated as systole time/√cycle time. Carotid blood flow was calculated as π × (carotid diameter) 2 /4 × velocity time integral × heart rate. Measurements were obtained using a single carotid waveform and an average of three carotid waveforms for both measures. Single waveform measurements of corrected flow time did not correlate with cardiac output ( ρ = 0.25, 95% CI −0.03 to 0.49, p = 0.08), but an average of three waveforms correlated significantly, although weakly ( ρ = 0.29, 95% CI 0.02–0.53, p = 0.046). Carotid blood flow measurements correlated moderately with cardiac output regardless of if single waveform or an average of three waveforms were used: ρ = 0.44, 95% CI 0.18–0.63, p = 0.004, and ρ = 0.41, 95% CI 0.16–0.62, p = 0.004, respectively. Carotid blood flow may be a better marker of cardiac output and less subject to measurements issues than corrected carotid flow time.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".