Data and code for "Initial head posture affects the neck muscle and head/neck kinematic responses during low-speed rear impacts"
Bibliographic record
Abstract
Data and code to replicate the figures in "Initial head posture affects the neck muscle and head/neck kinematic responses during low-speed rear impacts". Abstract Purpose The goal of this study was to quantify the effect of initial head posture on neck muscle activity and head/neck kinematics during rear impacts. Methods Twelve seated participants experienced rear impacts on a sled with their head in five initial driving postures: left shoulder check, left mirror check, neutral head-forward, rear-view mirror check, or looking at their front-seat passenger. Electromyographic activity in four neck muscles was recorded bilaterally with indwelling electrodes and normalized to maximum voluntary contraction (MVC) levels. Head and torso kinematics were measured. Results Pre-impact muscle activity increased in 6 of the 8 muscles for non-neutral postures compared to the neutral posture (Δ = 0.6–7.5% MVC). During impact, only the peak left multifidus activity significantly changed (Δ = − 12% MVC) during left mirror check compared to neutral posture. Compared to the neutral posture, we observed larger absolute head acceleration (Δ = 0.7–2.6 g) out of the sagittal plane for all non-neutral postures and smaller fore-aft head-torso displacement (Δ = 5.2–7.8 mm) in the left shoulder check and look-at-passenger postures, but only minimal changes in torso kinematics. Conclusion Despite minimal changes to peak neck muscle activity during impact, we observed widespread changes in the head kinematics in non-neutral postures. This work provides data to inform injury prevention methods and simulate drivers with non-neutral head postures in computational models.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.669 | 0.183 |
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".