Correlating head kinematics and cervical cerebrospinal fluid pressure transients in simulated whiplash exposures
Bibliographic record
Abstract
The origin and mechanics of whiplash injury from motor vehicle collisions are poorly understood. Among the proposed injury mechanisms, the inertial loading of the head and neck during whiplash exposures is theorized to produce injurious cerebrospinal fluid pressure (CSFP) transients. To better understand the mechanics and modal behavior of CSFP transients during whiplash exposures, we quantified the time-frequency relationship between input head kinematics and cervical CSFP responses in an in vivo pig model. Wavelet coherence analysis was used to correlate seven head kinematic parameters (including temporal Neck Injury Criterion, NIC) with CSFP during simulated extension and flexion whiplash exposures. Overall, the first and last 50 ms of exposures, and frequency ranges between 30-65 Hz had larger coherences between head kinematics and CSFP, with higher coherences in extension exposures than flexion exposures. NIC did not universally outperform other head kinematic parameters as a correlate of CSFP. These findings highlight the complexity of the dynamics involved in generating CSFP transients in the cervical spine during whiplash exposures.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".