Core sepsis-related competencies for medical students: an international consensus by Delphi technique
Bibliographic record
Abstract
BACKGROUND: Sepsis is a life-threatening condition which may arise from infection in any organ system and requires early recognition and management. Healthcare professionals working in any specialty may need to manage patients with sepsis. Educating medical students about this condition may be an effective way to ensure all future doctors have sufficient ability to diagnose and treat septic patients. However, there is currently no consensus on what competencies medical students should achieve regarding sepsis recognition and treatment. This study aims to outline what sepsis-related competencies medical students should achieve by the end of their medical student training in both high or upper-middle incomes countries/regions and in low or lower-middle income countries/regions. METHODS: Two separate panels from high or upper-middle income and low or lower-middle income countries/regions participated in a Delphi method to suggest and rank sepsis competencies for medical students. Each panel consisted of 13-18 key stakeholders of medical education and doctors in specialties where sepsis is a common problem (both specialists and trainees). Panelists came from all continents, except Antarctica. RESULTS: The panels reached consensus on 38 essential sepsis competencies in low or lower-middle income countries/regions and 33 in high or upper-middle incomes countries/regions. These include competencies such as definition of sepsis and septic shock and urgency of antibiotic treatment. In the low or lower-middle income countries/regions group, consensus was also achieved for competencies ranked as very important, and was achieved in 4/5 competencies rated as moderately important. In the high or upper-middle incomes countries/regions group, consensus was achieved in 41/57 competencies rated as very important but only 6/11 competencies rated as moderately important. CONCLUSION: Medical schools should consider developing curricula to address essential competencies, as a minimum, but also consider addressing competencies rated as very or moderately important.
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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 | 0.129 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".