A simple algorithm for differential diagnosis in hemodynamic shock based on left ventricle outflow tract velocity–time integral measurement: a case series
Bibliographic record
Abstract
Echocardiography has gained wide acceptance among intensive care physicians during the last 15 years. The lack of accredited formation, the long learning curve required and the excessive structural orientation of the present algorithms to evaluate hemodynamically unstable patients hampers its daily use in the intensive care unit. The aim of this article is to show 4 cases where the use of our simple algorithm based on VTI, was crucial. Subsequently, to explain the benefit of using the proposed algorithm with a more functional perspective, as a means for clinical decision-making. A simple algorithm based on left ventricle outflow tract velocity-time integral measurement for a functional hemodynamic monitoring on patients suffering hemodynamic shock or instability is proposed by Spanish Critical Care Ultrasound Network Group. This algorithm considers perfusion and congestion variables. Its simplicity might be useful for guiding physicians in their daily decision-making managing critically ill patients in hemodynamic shock.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".