Performance of the Cardiovascular Point of Care Ultrasound (POCUS) Exam by Internal Medicine Residents
Bibliographic record
Abstract
Background: Few studies have examined internal medicine residents' performance using cardiovascular point of care ultrasound (POCUS). Methods: From 2019 to 2022, first-year residents from two academic medical centers in Baltimore participated in the Assessment of Examination and Communication Skills (APECS). Interns examined a single patient with aortic insufficiency and were assessed on physical exam and POCUS technique, identifying physical exam and POCUS findings, generating a differential diagnosis, clinical judgment, and maintaining patient welfare. Spearman's correlation test was used to describe associations between clinical domains. Preceptor comments were examined to identify common errors in physical exam and POCUS exam technique and in identifying correct findings. Results: Fifty-three first-year residents (interns) performed a cardiovascular POCUS exam. Of these, 44 (83%) scored either "unsatisfactory" or "borderline" on their POCUS technique with a mean score of 29.5 (out of 100). Seventeen (32%) interns were able to correctly obtain a parasternal-long axis (PLAX) view with only 26 (52%) attempting an apical four-chamber (AP4) or subcostal (SUBC) view. Of the 11 participants who correctly obtained both PLAX and parasternal-short views (PSAX), 10 were able to properly identify a normal ejection fraction and the absence of a pericardial effusion. POCUS technique was statistically significantly associated with physical exam technique, identifying the correct POCUS findings, and generating a correct differential diagnosis (r=0.46, p<0.01; r=0.41, p=<0.01; r=0.60, p=<0.01, respectively). Conclusion: Internal medicine interns showed variable skill in performing and interpreting a cardiovascular POCUS exam. Further emphasis on teaching cardiovascular POCUS skills would likely increase ability to identify relevant cardiovascular findings and improve patient care.
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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".