Preliminary Experience With Inertial Movement Technology to Characterize Endotracheal Intubation Kinematics
Bibliographic record
Abstract
BACKGROUND: Endotracheal intubation (ETI) is an important emergency intervention. Only limited data describe ETI skill acquisition and often use bulky technology, not easily transitioned to the clinical setting. In this study, we used small, portable inertial detection technology to characterize intubation kinematic differences between experienced and novice intubators. METHODS: We performed a prospective study including novice (<10 prior clinical ETI) and experienced (>100 clinical ETI) emergency providers. We tracked upper extremity motion with roll, pitch, and yaw using inertial measurement units (IMU) placed on the bilateral hands and wrists of the intubator. Subject performed 6 simulated emergency intubations on a mannequin. Using machine learning algorithms, we determined the motions that best discriminated experienced and novice providers. RESULTS: We included data on 12 novice and 5 experienced providers. Four machine learning algorithms (artificial neural network, support vector machine, decision tree, and K-nearest neighbor search) were applied. Artificial neural network had the greatest accuracy (95% confidence interval) for discriminating between novice and experienced providers (91.17%, 90.8%-91.5%) and was the most parsimonious of the tested algorithms. Using artificial neural network, information from 5 movement features (right hand, roll amplitude; right hand, pitch amplitude; right hand, yaw standard deviation; left hand, yaw standard deviation; left hand, pitch frequency of peak amplitude) was able discriminated experienced from novice providers. CONCLUSIONS: Novice and experienced providers have different ETI movement patterns and can be distinguished by 5 specific movements. Inertial detection technology can be used to characterize the kinematics of emergency airway management.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".