Instrumented Pre-Hospital Care Simulation Mannequin for Use in Spinal Motion Restrictions Scenarios: Validation of Cervical and Lumbar Motion Assessment
Bibliographic record
Abstract
BACKGROUND: A mid-fidelity simulation mannequin, equipped with an instrumented cervical and lumbar spine, was developed to investigate best practices and train healthcare professionals in applying spinal motion restrictions (SMRs) during the early mobilization and transfer of accident victims with suspected spine injury. The study objectives are to (1) examine accuracy of the cervical and lumbar motions measured with the mannequin; and (2) confirm that the speed of motion has no bearing on this accuracy. METHODS: Accuracy was evaluated by concurrently comparing the orientation data obtained with the mannequin with that from an optoelectronic system. The mannequin's head and pelvis were moved in all anatomical planes of motion at different speeds. RESULTS: Accuracy, assessed by root-mean-square error, varied between 0.7° and 1.5° in all anatomical planes of motion. Bland-Altman analysis revealed a bias ranging from -0.7° to 0.6°, with the absolute limit of agreement remaining below 3.5°. The minimal detectable change varied between 1.3° and 2.6°. Motion speed demonstrated no impact on accuracy. CONCLUSIONS: The results of this validation study confirm the mannequin's potential to provide accurate measurements of cervical and lumbar motion during simulation scenarios for training and research on the application of SMR.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".