Taking the Pulse of POCUS: The State of Point-of-Care Ultrasound at a Pediatric Tertiary Care Hospital
Bibliographic record
Abstract
We aim to quantify and categorize point-of-care ultrasound (POCUS) usage by pediatric practitioners and trainees at our tertiary care center, and assess the degree of interest from pediatric residents, fellows, and program leaders for integrating POCUS into their training. Data was collected via online survey, evaluating the current use of POCUS in clinical decision making, desire for further formal training, and opinions on the importance of POCUS to future clinical practice. In total, 14 program directors/assistant program directors (PD/APDs) representing 10 of 15 training programs, 30 of 95 fellows representing 9 of 15 fellowships, and 32 of 82 residents responded. From PD/APDs, only 2 of the programs reported active use POCUS for clinical decision making, but 13 of the fellows and 9 residents reported doing so. In regard to desire for a formal POCUS program, 30.8% of PD/APDs, 43.8% of fellows without current curricula, and 87.5% of residents were interested in participating in such a program. When considering specialty, some non-acute care-based PD/APDs and fellows at our institution felt that POCUS was important to future practice. Pediatric subspecialty PD/APDs and their fellows had divergent outlooks on the importance of POCUS in future practice. Finally, an overwhelming majority of residents at our institution expressed a desire to learn, and half believing it will be important to future practice. Based on the degree of interest, medicolegal considerations, and trajectory of patient care, pediatric residency and fellowship programs should strongly consider integrating POCUS education into their curricula.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".