Effectiveness of Cervical Spine Stabilization Techniques
Bibliographic record
Abstract
OBJECTIVE: To compare head motions that occur when trained professionals perform the head squeeze (HS) and trap squeeze (TS) C-spine stabilization techniques. DESIGN: Cross-over design. PARTICIPANTS: Twelve experienced lead rescuers. MAIN OUTCOME MEASURES: Peak head motion with respect to initial conditions using inertial measurement units attached to the forehead and trunk of the simulated patient. We compared both HS and TS during lift-and-slide (L&S) and log-roll (LR) placement on spinal board, and agitated patient trying to sit up (AGIT-Sit) or rotate his head (AGIT-Rot). The a priori minimal important difference (MID) was 5 degrees for flexion or extension and 3 degrees for rotation or lateral flexion. RESULTS: The L&S technique was statistically superior to the LR technique. The only differences to exceed the MID were extension and rotation during LR (HS > TS). In the AGIT-Sit test scenario, differences in motion exceeded MID (HS > TS) for flexion, rotation, and lateral flexion. In the AGIT-Rot scenario, differences in motion exceeded MID for rotation only (HS >TS). There was similar intertrial variability of motion for HS and TS during L&S and LR but significantly more variability with HS compared with TS in the agitated patient. CONCLUSIONS: The L&S is preferable to the LR when possible for minimizing unwanted C-spine motion. There is little overall difference between HS and TS in a cooperative patient. When a patient is confused, the HS is much worse than the TS at minimizing C-spine motion.
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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 | 0.017 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".