Ultrasound in the surgical ICU: uses, abuses, and pitfalls
Bibliographic record
Abstract
PURPOSE OF REVIEW: Point-of-care ultrasound (POCUS) has become an integral component of daily care in the surgical ICU. There have been many novel advancements in the past two decades, too numerous to count. Many are of critical importance to the intensive care physician, whereas others are still accumulating evidence. Without appropriate training, diligence, and incorporation of the ultrasound findings into the whole clinical picture, this technique can be gravely misused. This review examines POCUS use in the surgical ICU, as well as highlights potential hazards and common pitfalls. RECENT FINDINGS: POCUS is essential for guidance of vascular access procedures, as well as in the characterization and treatment of respiratory failure, shock, and unstable blunt abdominal trauma. Ultrasound has growing evidence for rapidly evaluating many other diseases throughout the entire body, as well as guidance for procedures. Using advanced ultrasound techniques should only be done with corresponding levels of training and experience. SUMMARY: Ultrasound in the critical care setting has become an essential component of the assessment of most ICU patients. As more evidence accumulates, along with ever-increasing availability of ultrasound technology, its use will continue to expand. It, thus, behoves clinicians to not only ensure they are adept at obtaining and interpreting POCUS images but also efficiently incorporate these skills into holistic bedside care without delaying lifesaving therapies.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".