An Innovative Pediatric Chest Tube Insertion Task Trainer Simulation
Bibliographic record
Abstract
INTRODUCTION: Iatrogenic complications associated with chest tube insertion (CTI) could be related to the gaps in the procedural fidelity of the current CTI training models and their insufficiency to support training of procedural mastery. A CTI bench model simulation developed with reference to preexisting curriculum increases trainees' exposure and practice of this clinical skill. Newly developed training models need to be recognized by trainees as a usable learning device. In this report, we describe the development of a novel CTI model, based on curriculum, and survey its usability as a training model among pediatric trainees. METHODS: Based on the acute trauma life support curriculum for CTI and expert interview, a pediatric CTI task trainer (PCTITT) model was developed, piloted, and then implemented for usability by volunteer pediatric residents and pediatric emergency fellows in 2 procedural training courses. Participants responded to 11 questions designed to capture self-reported attitudes toward the usability of the PCTITT as a training model for CTI. Results were obtained using a subjective 5-point Likert scale. RESULTS: Of the 32 participants, we achieved a response rate of 75%. Of these respondents, 92% had some kind of CTI hands-on training in the past, and 50% had experience with a real patient. Of these respondents, 91% recommended this model for training, and 80% stated that this model was superior to previous models. CONCLUSIONS: A PCTITT is an easy to create and feasible bench top task trainer to teach CTI skills, which integrates with other simulations currently in use the process of teaching CTI. Trainees recognized it as usable and superior to previous models. Future work needs to focus on the improvement of model fidelity, skills transferability, and tool validation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".