Assessing nontechnical skills in otolaryngology emergencies through simulation‐based training
Bibliographic record
Abstract
OBJECTIVE: Nontechnical skills (NTS) are essential to emergency crisis management. Due to the rarity of true emergencies, they are challenging to teach and assess within a competency-based curriculum. Our purpose is to evaluate the utility of the Non-Technical Skills in Surgery (NOTSS) scale in NTS assessment in simulated otolaryngology and head and neck surgery (OTO-HNS) emergencies and identify common challenges that residents encounter. METHODS: Mixed methods analysis of 15 junior OTO-HNS resident teams in four simulated emergency scenarios. Six raters rated resident NTS performance using the NOTSS score. Constructivist-grounded theory was used to analyze scenario video transcripts to identify areas of learner difficulty to guide future simulation development. RESULTS: Residents scored highest in situational awareness and lowest in leadership domains. Raters showed good consistency and reliability overall (Cronbach's alpha = 0.885). There was no statistical difference in ratings between surgical experts and nonexperts. Qualitative analysis demonstrated challenges with closed-loop communication and handling transitions of leadership with the scenarios. CONCLUSION: Simulation-based training is an effective modality to teach NTS in crisis resource management. The NOTSS rating scale is a reliable instrument for assessing NTS in simulated OTO-HNS emergencies. Incorporating the NOTSS scale for NTS assessment within a competency-based curriculum is recommended. LEVEL OF EVIDENCE: NA. Laryngoscope, 128:2301-2306, 2018.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".