Prehospital Spinal Immobilization: Effect of Effort on Kinematics of Voluntary Head-neck Motion Assessed using Accelerometry
Bibliographic record
Abstract
INTRODUCTION: Standards for immobilizing potentially spine-injured patients in the prehospital environment are evolving. Current guidelines call for more research into treatment practices. Available research into spinal immobilization (SI) reveals a number of limitations. PROBLEM: There are currently few techniques for measuring head and neck motion that address identified limitations and can be adapted to clinically relevant scenarios. This study investigates one possible method. METHODS: Study participants were fitted with miniaturized accelerometers to record head motion. Participants were exposed to three levels of restraint: none, cervical-collar only, and full immobilization. In each condition, participants were instructed to move in single planes, with multiple iterations at each of four levels of effort. Participants were also instructed to move continuously in multiple planes, with iterations at each of three levels of simulated patient movement. Peak and average displacement and acceleration were calculated for each immobilization condition and level of effort. Comparisons were made with video-based measurement. Participant characteristics also were tracked. RESULTS: Acceleration and displacement of the head increased with effort and decreased with more restraint. In some conditions, participants generated measurable acceleration with minimal displacement. Continuous, multi-dimensional motions produced greater displacement and acceleration than single-plane motions under similar conditions. CONCLUSION: Study results suggest a number of findings: acceleration complements displacement as a measure of motion in potentially spine-injured patients; participant effort has an effect on outcome measures; and continuous, multi-dimensional motion can produce results that differ from single-plane motions. Miniaturized accelerometers are a promising technology for future research to investigate these findings in realistic, clinically relevant scenarios.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".