Competency‐Based Assessment Tool for Pediatric Tracheotomy: International Modified Delphi Consensus
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Create a competency-based assessment tool for pediatric tracheotomy. STUDY DESIGN: Blinded, modified, Delphi consensus process. METHODS: Using the REDCap database, a list of 31 potential items was circulated to 65 expert surgeons who perform pediatric tracheotomy. In the first round, items were rated as "keep" or "remove," and comments were incorporated. In the second round, experts were asked to rate the importance of each item on a seven-point Likert scale. Consensus criteria were determined a priori with a goal of 7 to 25 final items. RESULTS: The first round achieved a response rate of 39/65 (60.0%), and returned questionnaires were 99.5% complete. All items were rated as "keep," and 137 comments were incorporated. In the second round, 30 task-specific and seven previously validated global rating items were distributed, and the response rate was 44/65 (67.7%), with returned questionnaires being 99.3% complete. Of the Task-Specific Items, 13 reached consensus, 10 were near consensus, and 7 did not achieve consensus. For the 7 previously validated global rating items, 5 reached consensus and two were near consensus. CONCLUSIONS: It is feasible to reach consensus on the important steps involved in pediatric tracheotomy using a modified Delphi consensus process. These items can now be considered to create a competency-based assessment tool for pediatric tracheotomy. Such a tool will hopefully allow trainees to focus on the important aspects of this procedure and help teaching programs standardize how they evaluate trainees during this procedure. LEVEL OF EVIDENCE: 5 Laryngoscope, 130:2700-2707, 2020.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.142 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".