Pediatric Tracheostomy Emergency Readiness Assessment Tool: International Consensus Recommendations
Bibliographic record
Abstract
OBJECTIVE: To achieve consensus on critical steps and create an assessment tool for actual and simulated pediatric tracheostomy emergencies that incorporates human and systems factors along with tracheostomy-specific steps. METHODS: A modified Delphi method was used. Using REDCap software, an instrument comprising 29 potential items was circulated to 171 tracheostomy and simulation experts. Consensus criteria were determined a priori with a goal of consolidating and ordering 15 to 25 final items. In the first round, items were rated as "keep" or "remove". In the second and third rounds, experts were asked to rate the importance of each item on a 9-point Likert scale. Items were refined in subsequent iterations based on analysis of results and respondents' comments. RESULTS: The response rates were 125/171 (73.1%) for the first round, 111/125 (88.8%) for the second round, and 109/125 (87.2%) for the third round. 133 comments were incorporated. Consensus (>60% participants scoring ≥8, or mean score >7.5) was reached on 22 items distributed across three domains. There were 12, 4, and 6 items in the domains of tracheostomy-specific steps, team and personnel factors, and equipment respectively. CONCLUSIONS: The resultant assessment tool can be used to assess both tracheostomy-specific steps as well as systems factors affecting hospital team response to simulated and clinical pediatric tracheostomy emergencies. The tool can also be used to guide debriefing discussions of both simulated and clinical emergencies, and to spur quality improvement initiatives. LEVEL OF EVIDENCE: 5 Laryngoscope, 133:3588-3601, 2023.
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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 | 0.135 | 0.216 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.018 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.012 | 0.011 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".