Patterns of change of multisite pain over 1 year of follow‐up and related risk factors
Bibliographic record
Abstract
BACKGROUND: Multisite musculoskeletal pain is common and disabling. This study aimed to prospectively investigate the distribution of musculoskeletal pain anatomically, and explore risk factors for increases/reductions in the number of painful sites. METHODS: Using data from participants working in 45 occupational groups in 18 countries, we explored changes in reporting pain at 10 anatomical sites on two occasions 14 months apart. We used descriptive statistics to explore consistency over time in the number of painful sites, and their anatomical distribution. Baseline risk factors for increases/reductions by ≥3 painful sites were explored by random intercept logistic regression that adjusted for baseline number of painful sites. RESULTS: Among 8927 workers, only 20% reported no pain at either time point, and 16% reported ≥3 painful sites both times. After 14 months, the anatomical distribution of pain often changed but there was only an average increase of 0.17 painful sites. Some 14% workers reported a change in painful sites by ≥3. Risk factors for an increase of ≥3 painful sites included female sex, lower educational attainment, having a physically demanding job and adverse beliefs about the work-relatedness of musculoskeletal pain. Also predictives were as follows: older age, somatizing tendency and poorer mental health (each of which was also associated with lower odds of reductions of ≥3 painful sites). CONCLUSIONS: Longitudinally, the number of reported painful sites was relatively stable but the anatomical distribution varied considerably. These findings suggest an important role for central pain sensitization mechanisms, rather than localized risk factors, among working adults. SIGNIFICANCE: Our findings indicate that within individuals, the number of painful sites is fairly constant over time, but the anatomical distribution varies, supporting the theory that among people at work, musculoskeletal pain is driven more by factors that predispose to experiencing or reporting pain rather than by localized stressors specific to only one or two anatomical sites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".