Are cervical multifidus muscles active during whiplash and startle? An initial experimental study
Bibliographic record
Abstract
BACKGROUND: The cervical multifidus muscles insert onto the lower cervical facet capsular ligaments and the cervical facet joints are the source of pain in some chronic whiplash patients. Reflex activation of the multifidus muscle during a whiplash exposure could potentially contribute to injuring the facet capsular ligament. Our goal was to determine the onset latency and activation amplitude of the cervical multifidus muscles to a simulated rear-end collision and a loud acoustic stimuli. METHODS: Wire electromyographic (EMG) electrodes were inserted unilaterally into the cervical multifidus muscles of 9 subjects (6M, 3F) at the C4 and C6 levels. Seated subjects were then exposed to a forward acceleration (peak acceleration 1.55 g, speed change 1.8 km/h) and a loud acoustic tone (124 dB, 40 ms, 1 kHz). RESULTS: Aside from one female, all subjects exhibited multifidus activity after both stimuli (8 subjects at C4, 6 subjects at C6). Neither onset latencies nor EMG amplitude varied with stimulus type or spine level (p > 0.13). Onset latencies and amplitudes varied widely, with EMG activity appearing within 160 ms of stimulus onset (for at least one of the two stimuli) in 7 subjects. CONCLUSION: These data indicate that the multifidus muscles of some individuals are active early enough to potentially increase the collision-induced loading of the facet capsular ligaments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".