Risk factors for developing chronic whiplash disorders
Bibliographic record
Abstract
BACKGROUND: Whiplash injury is one of the most common injuries in traffic accidents. Most of the injured recover within three months, however, a significant number have symptoms much longer. OBJECTIVE: The aim of this study was to determine the basic epidemiological characteristics of whiplash (gender, age, collision type, the type of participants in an accident, clinical signs) on Bosnia and Herzegovina roads and identify risk factors for chronic symptoms. METHODS: The subjects of this retrospective study were traffic accident whiplash patients who were diagnosed, treated and monitored in a single hospital center. The initial examination was performed on the day or the day after the accident and follow-up examinations after four weeks, three months, and six months. RESULTS: Out of the 241 patients in this study, 14.1% had symptoms over six months after the trauma. 54.7% of the injured belong to the third and fourth decade and close to 80% were younger than 50 years. In addition to neck pain, the most common symptoms were limited neck mobility (69.7%), muscle spasms (63.5%), palpable pain of neck muscles (56%), headache (43.6%), nausea (23.7%). Statistical analysis showed a positive impact of age, Quebec Task Force (QTF) grade II, and more injury severity and cervical spine degenerative changes on prolonged recovery. CONCLUSIONS: The overwhelming majority of the injured belong to the working population. QTF2+ score is a useful indicator for prolonged recovery and chronic symptoms. Age, QTF score and degenerative changes of the cervical spine indicate an increased risk for poor recovery and chronic symptoms.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".