An Approach to Diversifying the Selection of a Guideline Panel—The Process Utilized for the Updated Adult Critical Care Ultrasound Guidelines
Bibliographic record
Abstract
OBJECTIVES: Clinical practice guidelines are essential for promoting evidence-based healthcare. While diversification of panel members can reduce disparities in care, processes for panel selection lack transparency. We aim to share our approach in forming a diverse expert panel for the updated Adult Critical Care Ultrasound Guidelines. DESIGN: This process evaluation aims to understand whether the implementation of a transparent and intentional approach to guideline panel selection would result in the creation of a diverse expert guideline panel. SETTING: This study was conducted in the setting of creating a guideline panel for the updated Adult Critical Care Ultrasound Guidelines. PATIENTS: Understanding that family/patient advocacy in guideline creations can promote the impact of a clinical practice guideline, patient representation on the expert panel was prioritized. INTERVENTIONS: Interventions included creation of a clear definition of expertise, an open invitation to the Society of Critical Care Medicine membership to apply for the panel, additional panel nomination by guideline leadership, voluntary disclosure of pre-identified diversity criteria by potential candidates, and independent review of applications including diversity criteria. This resulted in an overall score per candidate per reviewer and an open forum for discussion and final consensus. MEASUREMENTS AND MAIN RESULTS: The variables of diversity were collected and analyzed after panel selection. These were compared with historical data on panel composition. The final guideline panel comprised of 33 panelists from six countries: 45% women and 79% historically excluded people and groups. The panel has representation from nonphysician professionals and patients advocates. Of the healthcare professionals, there is representation from early, mid, and late career stages. CONCLUSIONS: Our intentional and transparent approach resulted in a panel with improved gender parity and robust diversity along ethnic, racial, and professional lines. We hope it can serve as a starting point as we strive to become a more inclusive and diverse discipline that creates globally representative guidelines.
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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 | 0.347 | 0.386 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".