Cervical Spine Motion during Transfer and Stabilization Techniques
Bibliographic record
Abstract
Objectives. To compare paramedics’ ability to minimize cervical spine motion during patient transfer onto a vacuum mattress with two stabilization techniques (head squeeze vs. trap squeeze) and two transfer methods (log roll with one assistant (LR2) vs. 3 assistants (LR4)). Methods. We used a crossover design to minimize bias. Each lead paramedic performed 10 LR2 transfers and 10 LR4 transfers. For each of the 10 LR2 and 10 LR4 transfers, the lead paramedic stabilized the cervical spine using the head squeeze technique five times and the trap squeeze technique five times. We randomized the order of the stabilization techniques and LR2/LR4 across lead paramedics to avoid a practice or fatigue effect with repeated trials. We measured relative cervical spine motion between the head and trunk using inertial measurement units placed on the forehead and sternum. Results. On average, total motion was 3.9° less with three assistants compared to one assistant (p = 0.0002), and 2.8° less with the trap squeeze compared to the head squeeze (p = 0.002). There was no interaction between the transfer method and stabilization technique. When examining specific motions in the six directions, the trap squeeze generally produced less lateral flexion and rotation motion but allowed more extension. Examining within paramedic differences, some paramedics were clearly more proficient with the trap squeeze technique and others were clearly more proficient with the head squeeze technique. Conclusion. Paramedics performing a log roll with three assistants created less motion compared to a log roll with only one assistant, and using the trap squeeze stabilization technique resulted in less motion than the head squeeze technique but the clinical relevance of the magnitude remains unclear. However, large individual differences suggest future paramedic training should incorporate both best evidence practice as well as recognition that there may be individual differences between paramedics.
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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 | 0.012 |
| 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 | 0.001 |
| 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".