Description and evaluation of an internal medicine diagnostic and procedural POCUS service
Bibliographic record
Abstract
Introduction: Point-of-care ultrasound (POCUS) is a technology with increasing utility and uptake in internal medicine (IM). While the majority of POCUS is performed by the treating clinician themselves, there is an evolving niche for expert-level POCUS users to act as consultants for diagnostic or procedural POCUS questions. There is minimal existing literature surrounding these types of consultant services. Methods: This was a retrospective service audit of an IM consultative POCUS team at an academic quaternary care hospital. The total number of scans, exam types, and quality assurance compliance was extracted from a POCUS-specific archiving system for a 12-month period. Individual reports from the POCUS team were reviewed from a 6-month period to examine clinical impact. Results: Over a 12-month period, 2010 POCUS exams were performed, with an average of 8.0 scans per workday. Of these, 1071 (53.3%) were reported on an archiving platform, and 810 (75.6%) of reported scans completed quality assurance review. Over a 6-month period, the IM POCUS service reported a total of 437 scans. The majority ( n = 272, 62.2%) were diagnostic scans, with the most common exam type being volume status assessments (33.6%). Of all consults, 82.8% led to a change in clinical management. Discussion: We describe a novel diagnostic and procedural IM POCUS consultative service. Benefits of this type of service include wider access to POCUS expertise for complex patients who benefit from detailed multi-organ assessments, expedited procedures, potential cost savings, and increased procedural and ultrasound exposure for trainees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".