The Correlation between Carotid Artery Corrected Flow Time and Velocity Time Integral during Central Blood Volume Loss and Resuscitation
Bibliographic record
Abstract
Abstract Background: Doppler ultrasound of the common carotid artery is used to infer central hemodynamics. For example, change in the common carotid artery corrected flow time (ccFT) and velocity time integral (VTI) are proposed surrogates of changing stroke volume. However, conflicting data exist which may be due to inadequate beat sample size and measurement variability – both intrinsic to handheld systems. In this brief communication, we determined the correlation between changing ccFT and carotid VTI during progressively severe central blood volume loss and resuscitation. Methods: Measurements were obtained through a novel, wireless, wearable Doppler ultrasound system. Sixteen participants (ages of 18–40 years with no previous medical history) were studied across 25 lower body-negative pressure protocols. Relationships were assessed using repeated-measures correlation regression models. Results: In total, 33,110 cardiac cycles comprise this analysis; repeated-measures correlation showed a strong, linear relationship between ccFT and VTI. The strength of the ccFT-VTI relationship was dependent on the number of consecutively averaged cardiac cycles (R 1 cycle = 0.70, R 2 cycles = 0.74, and R 10 cycles = 0.81). Conclusions: These results positively support future clinical investigations employing common carotid artery Doppler as a surrogate for central hemodynamics.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".