Effectiveness of cervical spine stabilisation techniques
Bibliographic record
Abstract
Background Proper stabilisation of suspected unstable spine injuries is necessary to prevent (worsen) spinal cord damage. Although the lift-and-slide (L&S) technique has been shown superior to the log-roll (LR) technique to place the body on the spinal board, no studies have yet compared different techniques of manual stabilisation of the c-spine itself. Objective To compare cervical motions that occur when trained professionals perform the Head Squeeze (HS) and Trap Squeeze (TS) c-spine stabilisation techniques. Design Cross-over. Setting and participants 12 experienced therapists. Assessment HS and TS during lift-and-slide (L&S) and LR placement on spinal board, and agitated patient trying to trying to sit up (AGIT-Sit) or rotate his head (AGIT-Rot). Main outcome measurements Peak head motion with respect to initial conditions using inertial measurement units attached to the forehead and trunk of the simulated patient. Comparisons between HS and TS with a priori minimal important difference (MID) of 5° for flexion or extension, and 3° for rotation or lateral flexion. Results Overall, 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 inter-trial variability of motion for HS and TS during L&S and LR, but significantly more variability with HS compared to TS in the agitated patient. Conclusion 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 and trying to move, 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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".