Whiplash Injury is More Than Neck Pain: A Population-Based Study of Pain Localization After Traffic Injury
Bibliographic record
Abstract
OBJECTIVES: To describe the distribution of bodily pain and identify common patterns of pain localization after traffic injury. METHODS: Cross-sectional analysis of a population-based cohort of 6481 Saskatchewan residents who were treated or filed an auto insurance claim within 30 days of traffic injury or both. The prevalence of pain in each of 13 body areas was calculated and compared with pain confined exclusively to each of these areas. Principal component analysis was used to identify the main patterns of pain localization after traffic injury. RESULTS: Irrespective of pain in other areas, 86% of respondents reported posterior neck pain, 72% indicated head pain, and 60% noted lumbar back pain. Ninety-five percent of claimants reported some pain within the posterior trunk region, comprising the posterior neck, shoulder, mid-back, lumbar, and buttock areas. Only 0.4% of respondents reported posterior neck pain only. Four main patterns accounted for 60% of the variance in pain localization: 1) upper anterior trunk and upper extremity pain; 2) head, posterior neck, and upper posterior trunk pain; 3) low back pain; and 4) lower anterior trunk and lower extremity pain. CONCLUSION: Pain after traffic injury is most commonly reported in multiple body areas; isolated neck pain is extremely rare. These results have implications for clinical management of traffic injuries and interpretation of whiplash-related trials.
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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 | 0.001 |
| 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".