Development of a Cost-Effective Pediatric Intubation Task Trainer for Rural Medical Education
Bibliographic record
Abstract
Pediatric intubation and airway management (PIAM) is a life-saving, emergent procedure that is performed by a variety of healthcare practitioners. Securing the pediatric airway in a time-sensitive fashion is a specialized skill that declines with lack of practice, leading to a precarious gap in clinical competency and healthcare delivery. However, current training models for PIAM, such as live animals, human cadavers, and simulators, are not adequately accessible or reliable due to their combination of high cost, unrealistic simulation, lack of standardization, and ethical concerns. Task trainers pose an ethically and fiscally sustainable training model for experiential learning through repetitive practice, which has been shown to dramatically improve trainee proficiency and confidence in performing high-acuity low-occurrence procedures such as pediatric intubation. This work aims to report the development process and initial validation evidence of a prototype cost-effective pediatric intubation task trainer that can be used for post-graduate education, especially in resource-challenged settings.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".