The Hybrid POCUS-to-Echo Feasibility Study: Provision of Expedited Cardiac Point of Care Ultrasound Service (e-POCUS) by the Echocardiography Lab
Bibliographic record
Abstract
Background: Comprehensive transthoracic echocardiography (CTE) provides information vital to the care of acutely ill and unstable patients, but may not be readily available. Cardiac point of care ultrasound (POCUS) is well suited to providing key information at the bedside to expedite decision making. Our objective was to evaluate the feasibility of expedited-POCUS (e-POCUS) provided by the echo lab for internal medicine, cardiology and intensive care services. Methods: A new e-POCUS service was developed by the Kingston Health Sciences Center Echo Lab, whereby focused information relevant to 4 clinical situations (acute heart failure, tamponade, shock and suspected acute valvulopathy) would be provided urgently at the bedside. Requests were acquired over a 4 month period. Sonographers were immediately deployed on request and followed a standard POCUS protocol for each scenario. Staff echocardiographers provided immediate interpretation and arranged for further imaging at their discretion. The response time, diagnostic accuracy and clinical utility of e-POCUS was assessed. Results: A total of 18 patients were evaluated. The average time of an e-POCUS exam was 10 minutes and the average e-POCUS to formal CTE timing was 1.3 days. The agreement between e-POCUS and CTE for the presence of segmental wall motion abnormalities was 83% (Kappa=0.61, p=0.009) and 72% for the detection of right ventricular dilatation (Kappa =0.44, p=0.058). The e-POCUS results altered the working diagnosis in 72% of cases. Conclusion: The provision of an e-POCUS service by the Echo Lab is a feasible workflow solution meeting the demands of a new practice pattern.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".