Auditory Startle Alters the Response of Human Subjects Exposed to a Single Whiplash-like Perturbation
Bibliographic record
Abstract
In Brief Study Design. Human volunteers were exposed to a single whiplash-like perturbation. Objective. To determine how muscle and kinematic responses are affected by the superposition of a rear-end collision and loud startling noise. Summary of Background Data. Many whiplash studies use forward perturbations without reproducing the sound of a car crash. Loud sounds are known to evoke startle responses in the neck muscles and therefore could affect whiplash injuries. Methods. Sixty-five subjects (30 female, 35 male) were exposed to a single forward horizontal perturbation. Head and torso kinematics, and electromyographic activity in the sternohyoid, sternocleidomastoid, scalenus, and cervical paraspinal muscles were measured. Two awareness conditions (deceived and unaware subjects) nested in two startle conditions (with or without a 40 milliseconds, 124 dB sound) were tested. Results. Startle and gender affected the amplitude and timing of numerous kinematic and muscle variables. Awareness affected only one muscle variable. Startled individuals exhibited greater peak head and trunk accelerations, increased activity of the cervical paraspinal muscles, and a reduced head retraction and trunk angle. Conclusions. An acoustic startle alters the neck muscle and kinematic responses and may be as important as gender in the genesis of whiplash injury. Acoustic startle responses evoked by rear-end collisions may affect whiplash injury potential. Here we show that neck muscle and head/neck kinematic responses intensify when a loud startling noise and a forward perturbation are superimposed, and that startle may be as important as gender in the genesis of whiplash injury.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".