The Diagnostic and Therapeutic Impact of Point-of-Care Ultrasonography in the Intensive Care Unit
Bibliographic record
Abstract
PURPOSE: In light of point-of-care ultrasonography's (POCUS) recent rise in popularity, assessment of its impact on diagnosis and treatment in the intensive care unit (ICU) is of key importance. METHODS: Ultrasound examinations were collected through an ultrasound reporting software in 6 multidisciplinary ICU units from 3 university hospitals in Canada and the United States. This database included a self-reporting questionnaire to assess the impact of the ultrasound findings on diagnosis and treatment. We retrieved the results of these questionnaires and analyzed them in relation to which organs were assessed during the ultrasound examination. RESULTS: One thousand two hundred and fifteen ultrasound studies were performed on 968 patients. Intensivists considered the image quality of cardiac ultrasound to be adequate in 94.7% compared to 99.7% for general ultrasound ( P < .001). The median duration of a cardiac examination was 10 (interquartile range [IQR] 10) minutes compared to 5 (IQR 8) minutes for a general examination ( P < .001). Overall, ultrasound findings led to a change in diagnosis in 302 studies (24.9%) and to a change in management in 534 studies (44.0%). A change in diagnosis or management was reported more frequently for cardiac ultrasound than for general ultrasound (108 [37.1%] vs 127 [16.5%], P < .001) and (170 [58.4%] vs 270 [35.1%], P < .001). Assessment of the inferior vena cava for fluid status emerged as the critical care ultrasound application associated with the greatest impact on management. CONCLUSION: Point-of-care ultrasonography has the potential to optimize care of the critically ill patients when added to the clinical armamentarium of the intensive care physician.
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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 | 0.012 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".