Factors Influencing Neck Pain Intensity in Whiplash-associated Disorders in Sweden
Bibliographic record
Abstract
OBJECTIVES: To investigate if sociodemographic and economic factors, preinjury health status, and collision factors are associated with initial neck pain intensity in whiplash-associated disorders (WAD) in Sweden. The factors of interest were demographic and socioeconomic factors, prior health, and collision factors. METHODS: A cohort study of car occupants, insured by either of 2 Swedish traffic insurers, age 18 to 74 years, who filed an injury claim and reported WAD after a motor vehicle collision (n=1187) were approached with mailed questionnaires. These contained questions about prior health, details about the collision, and symptoms after the collision. Neck pain intensity was measured on a visual analog scale and categorized into mild pain (0 to 30 mm), moderate pain (31 to 54 mm), and severe pain (55 to 100 mm). RESULTS: Low educational level [odds ratio (OR) 2.8; 95% confidence interval (CI) 1.8-4.5], being sole adult in the family (OR 1.6; 95%CI 1.1-2.2), prior neck pain (OR 2.9; 95%CI 1.4-6.2), prior headache (OR 2.2; 95%CI 0.7-6.9), prior poor general health (OR 2.6; 95%CI 1.4-4.8), and exposure to rollover collision (OR 1.9; 95%CI 1.0-3.8) were all associated with severe initial neck pain intensity. Most of these factors were also associated with moderate pain intensity. DISCUSSION: This study confirms results from a previous study that sociodemographic and economic status, preinjury health status, and collision-related factors are associated with participants' rating of initial neck pain intensity in WAD. The findings are of importance for interpreting and understanding the underlying factors of pain rating.
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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 | 0.000 | 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".