Delphi Consensus Recommendations for the Development of the Emergency Medicine Point of Care Ultrasound (POCUS) Curriculum in Nepal
Bibliographic record
Abstract
Introduction: Emergency Medicine Point of Care Ultrasound (EM-POCUS) is a diagnostic bedside tool for quick and accurate clinical decision-making. Comprehensive training in POCUS is a mandatory part of EM training in developed countries. In Nepal, we need to build an educational curriculum based on the local medical system, available resources, and educational environment. We used the modified Delphi method to develop a EM-POCUS curriculum. Methods: We formed an EM-POCUS core working group based on expertise in key identified areas. The core working group developed criteria for expert panelist selection and synthesized the data for panelists after each Delphi round. We recruited 46 expert panelists to participate in a series of electronic surveys. The literature review and the results of the first Delphi round identified a set of competencies. Quantitative methodology was performed for subsequent surveys. Data analysis of the frequency, percentage, median, and interquartile range of the 9-point Likert scale was performed. We deemed a minimum threshold of 80% agreement to retain items across Delphi rounds. The result of every round was disseminated before subsequent rounds for the expert panelists to review responses in light of the group’s response. Results: We identified 10 specific global competency categories and 132 objectives (Round 1, response rate 85%). Rounds 2 and 3 (response rates 78% and 81% respectively) developed consensus on 45 core objectives (34%). The list of EM-POCUS competencies with the median (IQR) was finalized. Conclusion: This expert, consensus-generated EM-POCUS curriculum provides detailed guidance for EM-POCUS education and applications in clinical practice in Nepal.
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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 | 0.439 | 0.351 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.011 | 0.022 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".