Is It Time for a Population Health Approach to Neck Pain?
Bibliographic record
Abstract
OBJECTIVE: Neck pain and its associated disorders (NPAD) cause significant health burden in the general population and after road traffic and occupational injury. Individual-level health care treatments have been well studied, but population-health approaches to this problem have not. We used a best-evidence synthesis to examine population-level approaches to the prevention and control of NPAD. METHODS: The systematic review examined studies published between 1980 and 2006 that addressed the incidence, prevalence, risk factors, prevention, cost, assessment and classification, interventions, and course and prognostic factors for NPAD. Citations were screened for relevance, scientifically reviewed, and synthesized. Valid studies addressing public policies or population-level approaches to the prevention and control of NPAD were identified and used in the evidence synthesis. RESULTS: Only 8 of the 552 scientifically admissible studies were considered relevant to a public or population health approach to preventing and controlling the burden of NPAD. For whiplash-associated disorders, active head restraints and seat backs were protective in rear-end collisions; insurance policies affected the incidence and recovery; government funding of multidisciplinary rehabilitation programs did not benefit recovery; and early intensive health care delayed recovery. In the workplace, 2 randomized trials failed to show any preventive effect for ergonomic interventions or physical training and stress management. One study documented the societal cost of neck pain. CONCLUSIONS: There is little evidence on which to make public or population-level recommendations, despite the important public health burden and costs of NPAD. Population-level approaches to preventing and controlling NPAD should be investigated.
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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 | 0.148 | 0.306 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.013 | 0.030 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".