Continuous Venous-Arterial Doppler Ultrasound During a Preload Challenge
Bibliographic record
Abstract
A preload challenge (PC) is a clinical maneuver that, first, increases the cardiac filling (i.e., preload) and, second, calculates the change in cardiac output. Fundamentally, a PC is a bedside approach for testing the Frank-Starling-Sarnoff (i.e., "cardiac function") curve. Normally, this curve has a steep slope such that a small change in the cardiac preload generates a large change in the stroke volume (SV) or cardiac output. However, in various disease states, the slope of this relationship flattens such that increasing the volume into the heart leads to little rise in the SV. In this pathological scenario, additional cardiac preload (e.g., intravenous fluid) is unlikely to be physiologically effective and could lead to harm if organ congestion evolves. Therefore, inferring both the cardiac preload and output is clinically useful as it may guide intravenous (IV) fluid resuscitation. Accordingly, the goal of this protocol is to describe a method for contemporaneously tracking the surrogates of cardiac preload and output using a novel, wireless, wearable ultrasound during a well-validated preload challenge.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".