Optimising flow without congestion using the venous‐arterial Doppler enhanced resuscitation framework
Bibliographic record
Abstract
Introduction: Ultrasonography as a guide for intravenous (IV) fluid therapy is increasingly accepted within the spheres of acute care. Initial investigations and protocols often focused on measures of arterial flow as an objective approach for personalising organ 'perfusion.' More recently, and with literature associating excessive IV fluid with adverse outcomes, venous ultrasound as a measure of organ 'congestion' is taking hold. Yet, arterial (i.e., 'perfusion') and venous (i.e., 'congestion') Doppler ultrasound measures are often performed separately and can be time-consuming, especially for novices. Methods: We report a case, wherein venous and arterial Doppler were simultaneously measured using a wireless, wearable ultrasound as a means to optimise flow without congestion. Results: Before IV volume expansion, the patient had Doppler measures consistent with low central venous pressure (CVP) and stroke volume (SV). Following IV volume expansion, venous Doppler remained the same; however, carotid corrected flow time (ccFT) increased significantly. Conclusion: A framework for venous-arterial Doppler enhanced resuscitation (VADER) can be used to guide IV volume in patients at risk for venous congestion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".