The role of point-of-care ultrasound to assess fluid responsiveness and fluid tolerance in the intensive care unit
Bibliographic record
Abstract
Fluid accumulation is epidemiologically associated with adverse outcomes in various clinical contexts. Assessing fluid responsiveness identifies conditions where intravenous fluids can increase cardiac output, improve organ blood supply during hypoperfusion and prevent the administration of ineffective fluids with deleterious effects. Point-of-care ultrasound (POCUS) enables fluid administration guided by fluid responsiveness, serving as one of the few non-invasive technological aids widely accessible both within and outside the intensive care unit. In this review, we focus on how POCUS can complement the evaluation of fluid responsiveness and fluid tolerance. The topics include a review of POCUS techniques to estimate the change in cardiac output following preload modifying manoeuvers, evaluation of thoracic fluid tolerance through lung ultrasound, and evaluation of systemic fluid tolerance through venous Doppler and the venous excess ultrasound (VExUS) assessment.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".