Deceleration during 'real life' motor vehicle collisions – a sensitive predictor for the risk of sustaining a cervical spine injury?
Bibliographic record
Abstract
BACKGROUND: The predictive value of trauma impact for the severity of whiplash injuries has mainly been investigated in sled- and crash-test studies. However, very little data exist for real-life accidents. Therefore, the predictive value of the trauma impact as assessed by the change in velocity of the car due to the collision (DeltaV) for the resulting cervical spine injuries were investigated in 57 cases after real-life car accidents. METHODS: DeltaV was determined for every car and clinical findings related to the cervical spine were assessed and classified according to the Quebec Task Force (QTF). RESULTS: In our study, 32 (56%) subjects did not complain about symptoms and were therefore classified as QTF grade 0; 25 (44%) patients complained of neck pain: 8 (14%) were classified as QTF grade I, 6 (10%) as QTF grade II, and 11 (19%) as QTF grade IV. Only a slight correlation (r = 0.55) was found between the reported pain and DeltaV. No relevant correlation was found between DeltaV and the neck disability index (r = 0.46) and between DeltaV and the QTF grade (r = 0.45) for any of the collision types. There was no DeltaV threshold associated with acceptable sensitivity and specificity for the prognosis of a cervical spine injury. CONCLUSION: The results of this study indicate that DeltaV is not a conclusive predictor for cervical spine injury in real-life motor vehicle accidents. This is of importance for surgeons involved in medicolegal expertise jobs as well as patients who suffer from whiplash-associated disorders (WADs) after motor vehicle accidents. TRIAL REGISTRATION: The study complied with applicable German law and with the principles of the Helsinki Declaration and was approved by the institutional ethics commission.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".