Course and Prognostic Factors for Neck Pain in Workers
Bibliographic record
Abstract
STUDY DESIGN: Best-evidence synthesis. OBJECTIVE: To perform a best evidence synthesis on the course and prognostic factors for neck pain and its associated disorders in workers. SUMMARY OF BACKGROUND DATA: Knowledge of the course of neck pain in workers guides expectations for recovery. Identifying prognostic factors assists in planning effective workplace policies, formulating interventions and promoting lifestyle changes to decrease the frequency and burden of neck pain in the workplace. METHODS: The Bone and Joint Decade 2000-2010 Task Force on Neck Pain and its Associated Disorders (Neck Pain Task Force) conducted a critical review of the literature published between 1980 and 2006 to assemble the best evidence on neck pain and its associated disorders. Studies meeting criteria for scientific validity were included in a best evidence synthesis. RESULTS: We found 226 articles related to course and prognostic factors in neck pain and its associated disorders. After a critical review, 70 (31%) were accepted on scientific merit; 14 of these studies related to course and prognostic factors in working populations. Between 60% and 80% of workers with neck pain reported neck pain 1 year later. Few workplace or physical job demands were identified as being linked to recovery from neck pain. However, workers with little influence on their own work situation had a slightly poorer prognosis, and white-collar workers had a better prognosis than blue-collar workers. General exercise was associated with better prognosis; prior neck pain and prior sick leave were associated with poorer prognosis. CONCLUSION: The Neck Pain Task Force presents a report of current best evidence on course and prognosis for neck pain. Few modifiable prognostic factors were identified; however, having some influence over one's own job and being physically active seem to hold promise as prognostic factors.
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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 | 0.021 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".