The correlation between carotid artery doppler and stroke volume during central blood volume loss and resuscitation
Bibliographic record
Abstract
BACKGROUND: Using peripheral arteries to infer central hemodynamics is common among hemodynamic monitors. Doppler ultrasound of the common carotid artery has been used in this manner with conflicting results. We investigated the relationship between changing common carotid artery Doppler measures and stroke volume (SV), hypothesizing that more consecutively-averaged cardiac cycles would improve SV-carotid Doppler correlation. METHODS: Twenty-seven healthy volunteers were recruited and studied in a physiology laboratory. Carotid artery Doppler pulse was measured with a wearable, wireless ultrasound during central hypovolemia and resuscitation induced by a stepped lower body negative pressure protocol. The change in maximum velocity time integral (VTI) and corrected flow time of the carotid artery (ccFT) were compared with changing SV using repeated measures correlation. RESULTS: In total, 73,431 cardiac cycles were compared across 27 subjects. There was a strong linear correlation between changing SV and carotid Doppler measures during simulated hemorrhage (repeated-measures linear correlation [Rrm ]=0.91 for VTI; 0.88 for ccFT). This relationship improved with larger numbers of consecutively-averaged cardiac cycles. For ccFT, beyond four consecutively-averaged cardiac cycles the correlation coefficient remained strong (i.e., Rrm of at least 0.80). For VTI, the correlation coefficient with SV was strong for any number of averaged cardiac cycles. For both ccFT and VTI, Rrm remained stable around 25 consecutively-averaged cardiac cycles. CONCLUSIONS: There was a strong linear correlation between changing SV and carotid Doppler measures during central blood volume loss. The strength of this relationship was dependent upon the number of consecutively-averaged cardiac cycles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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".