Procedural sedation competencies: a review and multidisciplinary international consensus statement on knowledge, skills, training, and credentialing
Bibliographic record
Abstract
Procedural sedation is practised by a heterogeneous group of practitioners working in a wide array of settings. However, there are currently no accepted standards for the competencies a sedation practitioner should have, the content of sedation training programmes, and guidelines for credentialing. The multidisciplinary International Committee for the Advancement of Procedural Sedation sought to develop a consensus statement on the following: which competencies should medical or dental practitioners have for procedural sedation and how are they obtained, assessed, maintained, and privileged. Using the framework of Competency-Based Medical Education, the practice of procedural sedation was defined as a complex professional task requiring demonstrable integration of different competencies. For each question, the results of a literature review were synthetised into preliminary statements. Following an iterative Delphi review method, final consensus was reached. Using multispeciality consensus, we defined procedural sedation competence by identifying a set of core competencies in the domains of knowledge, skills, and attitudes across physical safety, effectiveness, psychological safety, and deliberate practice. In addition, we present a standardised framework for competency-based training and credentialing of procedural sedation practitioners.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".