Patterns of acute whiplash-associated disorder in the Lithuanian population after road traffic accidents
Bibliographic record
Abstract
OBJECTIVE: To investigate acute whiplash-associated disorder in the Lithuanian population who are unaware of the phenomenon. DESIGN: Controlled cohort study. SUBJECTS/PATIENTS: Seventy-one patients were enrolled from the emergency departments of the Kaunas region of Lithuania following road traffic accidents, examined within 3-14 days after the accident, and compared with 53 matched controls. METHODS: Clinical neurological examination, including range of motion and motion-evoked pain or stiffness in the neck; spontaneous pain and pain pressure threshold. Questionnaires: Quebec Task Force questionnaire (QTFQ); Disability Rating Index (DRI); Cognitive Failures Questionnaire (CFQ); Hospital Anxiety and Depression Scale (HADS) and health perception. RESULTS: Sixty-six of 71 (93%) patients developed acute symptoms. The most frequent symptoms found after road traffic accidents were neck or shoulder pain; reduced or painful neck movements, including decreased range of motion; multiple subjective symptoms according to QTFQ and significantly reduced pain threshold. Perceived health status was decreased and DRI was increased, while HADS showed a significantly higher risk of developing anxiety. Higher grade whiplash-associated disorder was linked with a greater reduction in range of motion and more prominent neck pain. CONCLUSION: Road traffic accidents induce whiplash-associated disorder in patients who seek help, but who are unaware of the condition whiplash-associated disorder. Whiplash-associated disorder should be considered and treated as an entity per se.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".