Point-of-Care Ultrasound (POCUS) Literature Primer: Key Papers on Renal and Biliary POCUS
Bibliographic record
Abstract
Objective The objective of this study is to identify the top five influential papers published on renal point-of-care ultrasound (POCUS) and the top five influential papers on biliary POCUS in adult patients. Methods A 14-member expert panel was recruited from the Canadian Association of Emergency Physicians (CAEP) Emergency Ultrasound Committee and the Canadian Ultrasound Fellowship Collaborative. All panel members have had ultrasound fellowship training or equivalent, are actively engaged in POCUS scholarship, and are involved with POCUS at their local site and nationally in Canada. We used a modified Delphi process consisting of three rounds of sequential surveys and discussion to achieve consensus on the top five influential papers for renal POCUS and biliary POCUS. Results The panel identified 27 relevant papers on renal POCUS and 30 relevant papers on biliary POCUS. All panel members participated in all three rounds of the modified Delphi process, and after completing this process, we identified the five most influential papers on renal POCUS and the five most influential papers on biliary POCUS. Conclusion We have developed a list, based on expert opinion, of the top five influential papers on renal and biliary POCUS to better inform all trainees and clinicians on how to use these applications in a more evidence-based manner. This list will also be of interest to clinicians and researchers who strive to further advance the field of POCUS.
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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 | 0.109 | 0.261 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.037 | 0.022 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".