Factors Influencing Neck Pain Intensity in Whiplash-Associated Disorders
Bibliographic record
Abstract
In Brief Study Design. Cohort study of subjects with whiplash-associated disorders (WAD). Objective. To assess the association between preinjury factors and neck pain intensity within 30 days after a motor vehicle collision. Summary of Background Data. Neck pain is the main symptom in WAD. There are studies of nonspecific musculoskeletal pain suggesting that pain intensity can be modified by psychologic, personal, or social factors, but, to our knowledge, no studies have investigated the association between such factors and neck pain intensity in WAD. Methods. The subjects (n = 5970) either filed a claim or were treated for neck pain within 30 days after a collision. Neck pain intensity was measured on the visual analog scale. Results. Fair or poor health before the collision was associated with severe neck pain in females (odds ratio 4.0, 95% confidence interval 1.8–8.9). Other associated factors in females included low education and prior neck pain. Low family income was associated with severe neck pain in males (odds ratio 2.3, 95% confidence interval 1.5–3.4), as was prior headache and being unaware of the head position at the time of collision. Conclusion. The results suggest that neck pain intensity in WAD seems to be influenced by several factors other than characteristics related to the injury event itself. A study of whiplash-associated disorder injuries investigated the relationship between noncollision related and collision-related factors, and the outcome neck pain intensity. The results suggest that pain intensity is affected by socioeconomic and preinjury health-related factors, as well as collision-related factors. Gender is an important determinant of which factors are important.
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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 | 0.001 |
| 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 | 0.001 | 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".