Point of care ultrasound (POCUS) applications in the ambulatory internal medicine setting: A scoping review
Bibliographic record
Abstract
INTRODUCTION: Point of care ultrasound (POCUS) is an increasingly recognized tool in internal medicine, with evidence supporting its use largely derived in the inpatient setting. We endeavored to provide an overview of outpatient POCUS applications that could be relevant to the practice of internal medicine. METHODS: A scoping review was conducted using the JBI Evidence Synthesis framework. Studies of adults who received a POCUS assessment during an outpatient clinic encounter for an indication that was within the scope of practice of an internist were included. Data was extracted on POCUS intervention characteristics, POCUS applications, as well as outcomes such as validation, feasibility, prognostic value, clinical integration, resource utilization, and effect on medical management. RESULTS: A total of 100 studies were included in the final analysis. Most POCUS scans were conducted by cardiologists (n = 21) and family physicians (n = 31). The primary POCUS applications identified were lung (n = 30), cardiac (n = 29), vascular (n = 10), and musculoskeletal (n = 9). Screening applications for cardiac and vascular arterial disease were also uncovered (n = 16). POCUS was found to have prognostic value (n = 16/21, 76.2 %) and be feasible (n = 40/40, 100.0 %) in studies of various outpatient settings. Just over half of the studies reported POCUS interventions that were integrated into clinical care (n = 56), with many of these studies reporting that POCUS was associated with changes in medical management (n = 35/36, 97.2 %) and reduced resource utilization (n = 18/19, 94.7 %). CONCLUSION: POCUS use in the outpatient internal medicine setting is a promising modality to aid in the screening, early diagnosis, and prognostication of various pathologies.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".