Incidence and Outcome of Whiplash Injury After Multiple Trauma
Bibliographic record
Abstract
STUDY DESIGN: A retrospective study of 101 consecutive polytrauma patients with regard to whiplash injury. OBJECTIVES: To investigate the incidence and evaluate long-term outcome of whiplash injury following high-energy trauma. SUMMARY OF BACKGROUND DATA: Chronic whiplash injury has been widely reported in the literature, following low-energy trauma. Very few studies exist on whiplash injury following high-energy trauma. METHODS: A total of 101 consecutive polytrauma patients admitted to our Level I Trauma Center over a 2-year period, fulfilling the inclusion criteria (age >18 years, high-energy trauma [a fall from a height >2 m, road traffic accidents with speed >30 km/h], and Injury Severity Score >16), were assessed. Whiplash injury was defined according to Quebec Task Force guidelines. The study group (n = 13) included patients who developed whiplash injury symptoms and the control group (n = 88) those who did not. The Neck Disability Index was calculated as an outcome measure for patients complaining of whiplash injury symptoms. The mean follow-up was 17 months. The chi2 and Student t tests were used for the statistical analysis (SPSS 12.1; SPSS, Inc., Chicago, IL). RESULTS: Only 13 out of 101 patients (1 female/12 male) (13%) complained of whiplash injury. There was a significantly higher rate of neck pain at triage (P < 0.001) and higher combined mean of Abbreviated Injury Score of upper torso (P < 0.0001) in the study group, elucidating the cause of whiplash injury. The Neck Disability Index was <24 points, indicating only mild-to-moderate disability in these patients. Whiplash injury incidence in this study (13%) was similar to the incidence of neck pain in the general population. CONCLUSIONS: The incidence of whiplash injury following polytrauma was found to be low in our study. There is no dose-response relation between magnitude of trauma severity and incidence 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".