Detecting the Change in Total Circulatory Flow with a Wireless, Wearable Doppler Ultrasound Patch: A Pilot Study
Bibliographic record
Abstract
Measuring fluid responsiveness is important in the management of critically ill patients, with a 10-15% change in cardiac output typically being used to indicate "fluid responsiveness." Ideally, these changes would be measured noninvasively and peripherally. The aim of this study was to determine how the common carotid artery (CCA) maximum velocity changes with total circulatory flow when confounding factors are mitigated and determine a value for CCA maximum velocity corresponding to a 10% change in total circulatory flow. DESIGN: Prospective observational pilot study. SETTING: Patients undergoing elective, on-pump coronary artery bypass grafting (CABG) surgery. PATIENTS: Fourteen patients were referred for elective coronary artery bypass grafting surgery. INTERVENTIONS: Cardiopulmonary bypass (CPB) pump flow changes during surgery, as chosen by the perfusionist. MEASUREMENTS: A hands-free, wearable Doppler patch was used for CCA velocity measurements with the aim of preventing user errors in ultrasound measurements. Maximum CCA velocity was determined from the spectrogram acquired by the Doppler patch. CPB flow rates were recorded as displayed on the CPB console, and further measured from the peristaltic pulsation frequency visible on the recorded Doppler spectrograms. MAIN RESULTS: Changes in CCA maximum velocity tracked well with changes in CPB flow. On average, a 13.6% change in CCA maximum velocity was found to correspond to a 10% change in CPB flow rate. CONCLUSIONS: Changes in CCA velocity may be a useful surrogate for determining fluid responsiveness when user error can be mitigated.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".