The startle response during whiplash: a protective or harmful response?
Bibliographic record
Abstract
Whiplash injuries are common following rear-end collisions. During such collisions, initially relaxed occupants exhibit brisk, stereotypical muscle responses consisting of postural and startle responses that may contribute to the injury. Using prestimulus inhibition, we sought to determine if the startle response elicited during a rear-end collision contributes to head stabilization or represents a potentially harmful overreaction of the body. Three experiments were performed. In the first two experiments, two groups of 14 subjects were exposed to loud tones (124 dB) preceded by prestimulus tones at either four interstimulus intervals (100-1,000 ms) or five prestimulus intensities (80-124 dB). On the basis of the results of the first two experiments, 20 subjects were exposed to a simulated rear-end collision (peak sled acceleration = 2 g; speed change = 0.75 m/s) preceded by one of the following: no prestimulus tone, a weak tone (85 dB), or a loud tone (105 dB). The prestimulus tones were presented 250 ms before sled acceleration onset. The loud prestimulus tone decreased the amplitude of the sternocleidomastoid (16%) and cervical paraspinal (29%) muscles, and key peak kinematics: head retraction (17%), horizontal head acceleration (23%), and head angular acceleration in extension (23%). No changes in muscle amplitude or kinematics occurred for the weak prestimulus. The reduced muscle and kinematic responses observed with loud tones suggest that the startle response represents an overreaction that increases the kinematics in a way that potentially increases the forces and strains in the neck tissues. We propose that minimizing this overreaction during a car collision may decrease the risk of whiplash injuries.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".