Predictive risk factors for chronic regional and multisite musculoskeletal pain: A 5-year prospective study in a working population
Bibliographic record
Abstract
The role of psychosocial and physical factors in the development of musculoskeletal pain (MSP) has now been clearly demonstrated. However, it is unclear whether these factors contribute to specific regional MSP or to multisite pain. The main goal of this study was to assess the impact of work-related factors according to gender on the development of regional and multisite MSP. A total of 12,591 subjects (65% men and 35% women) who were born in 1938, 1943, 1948, and 1953 and were participating in a French longitudinal prospective epidemiological survey (ESTEV) in 1990 to 1995 were eligible. Personal factors and work exposure were assessed by self-administered questionnaires. Statistical associations between chronic MSP (regional body site or multisite), personal factors, and occupational factors were analyzed using logistic regression modeling. The incidence of regional MSP and multisite pain in 1995 were, respectively, 17% and 25.6%. For women, highly repetitive movements predicted neck/shoulder pain; posture and vibrations predicted arm and low back pain; and effort with tools predicted arm pain. For men, forceful effort and vibrations predicted neck/shoulder pain; posture and forceful effort predicted lower limb and low back pain; and forceful effort and effort with tools predicted arm pain. Physical constraints (ie, forceful effort or vibrations) were associated with multisite pain in both genders. Only for women, psychological factors were risk factors predictive of upper limb pain and in 3 or 4 painful anatomical sites. These results support the hypothesis that some physical and psychological work-related factors are predictive of regional or multisite MSP but differ according to gender. Gender differences and risk factors for work-related musculoskeletal pain should be also taken into account to more effectively target preventive measures.
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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 | 0.005 | 0.003 |
| 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".