The epidemiology of neck pain: what we have learned from our population-based studies
Bibliographic record
Abstract
BACKGROUND: There are few population-based studies on the epidemiology of neck pain in the general population. PURPOSE: To synthesize the findings of two large population-based studies of the epidemiology of neck pain and whiplash-associated disorders from the province of Saskatchewan, Canada. STUDY DESIGN AND METHODS: We conducted two population-based cohort studies of neck pain and its related disability in Saskatchewan, Canada. First, the Saskatchewan Health and Back Pain Survey was designed to determine the prevalence and factors associated with neck pain in randomly selected adults. Second, we conducted a cohort study of the incidence and prognosis of whiplash and studied whether a change in the insurance system from tort to no-fault was related to a reduction in the number of whiplash claims and faster recovery. RESULTS: In 1995, the six-month prevalence of neck pain was 54.2% and 4.6% of adults experienced disabling neck pain in the previous six-months. Neck pain was associated with education, comorbidities, smoking, self-reported general health and a history of neck injury in a motor vehicle collision. The incidence of treated and/or compensated whiplash injury was estimated at 834/100,000 adults in 1994, and dropped by 28% to 598/100,000 adults in 1995, after tort reform. Compared to tort, the median time-to-recovery was more than 230 days faster under no-fault. The strongest predictors of recovery were age, gender, education, injury severity, lawyer involvement and type of initial care provider. CONCLUSION: Neck pain is a public health problem. The incidence and prognosis of whiplash injuries are greatly influenced by compensation for pain and suffering, legal factors, injury severity and sociodemographic characteristics. Overall, neck pain is a multifaceted disabling problem that deserves more attention. When treating patients with neck pain, clinicians need to recognize that it is more than a physical problem and that its prognosis is influenced by broader determinants of health.
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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 | 0.050 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".