Bibliographic record
Abstract
TOPIC IMPORTANCE: Accurate assessment of a patient's volume status is crucial in many conditions, informing decisions on fluid prescribing, vasoactive agents, and decongestive therapies. Determining a patient's volume status is challenging because of limitations in examination and investigations and the complexities of fluid homeostasis in disease states. Point-of-care ultrasound (POCUS) is useful in assessing hemodynamic parameters related to volume status, fluid responsiveness, and fluid tolerance. It requires understanding several physiologic concepts to interpret and integrate POCUS findings accurately into volume-related clinical decision-making. REVIEW FINDINGS: The following concepts serve as a scaffold for a comprehensive volume status assessment: central venous pressure, right-sided heart function, left-sided heart assessment, extravascular volume, and venous congestion. POCUS allows us access to these hemodynamic and structural data points as an extension and refinement of the physical examination. Often, multiple POCUS applications are used, and findings must be integrated with the rest of the clinical evaluation. We illustrate this using 3 common scenarios: hypotension, hypoxia, and acute kidney injury. Clinicians must be aware of the strengths and weaknesses of findings in different physiologic states and the potential pitfalls of image acquisition and interpretation. Further studies are necessary to determine the benefits and clinical outcomes of a POCUS-directed volume status assessment. SUMMARY: Volume status assessment is ubiquitous, yet is challenging to perform. This review summarizes foundational physiologic concepts relevant to volume status evaluation and highlights how multiorgan POCUS elucidates hemodynamic parameters that can be combined with the conventional clinical assessment to make fluid-related decisions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".