A wearable carotid Doppler tracks changes in the descending aorta and stroke volume induced by <scp>end‐inspiratory</scp> and <scp>end‐expiratory</scp> occlusion: A pilot study
Bibliographic record
Abstract
Abstract Background and Aims To test the feasibility of a novel, wearable carotid Doppler ultrasound to track changes in cardiac output induced by end‐inspiratory and end‐expiratory occlusion tests. Methods We observed the pattern of Doppler change of the common carotid artery during a simulated end‐inspiratory and expiratory occlusion test (sEIOT/sEEOT) in 10, nonventilated, healthy subjects. Simultaneously, we measured the Doppler signal of the descending aorta using duplex ultrasound (Xario, Toshiba Medical Systems) and stroke volume (SV) using noninvasive pulse contour analysis (Clearsight, Edwards Lifesciences, Irvine, California). Results During sEIOT, SV, maximum velocity time integral (VTI) of the descending aorta, and common carotid fell by 25.7% (P = .0131), 26.1% (P < .0001), and 18.5% (P < .0001), respectively. During sEEOT, SV, maximum VTI of the descending aorta, and common carotid rose by: 41.3% (P = .0051), 28.3% (P < .0001), and 41.6% (P < .0001), respectively. There was good correlation between change in aortic VTI and carotid VTI (r2 = 0.79); SV and aortic VTI (r2 = 0.82), and SV and carotid VTI (r2 = 0.95).The coefficient of variation of the VTI measured by the Doppler patch was roughly 60% less than that of the duplex system. Conclusions The pattern of SV change induced by a sEIOT/sEEOT in nonmechanically ventilated volunteers is reflected in the common carotid artery and descending aorta. The VTI variability of the Doppler patch was less than that of the traditional, duplex Doppler.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".