What Characterizes Persons Who Do Not Report Musculoskeletal Pain? Results from a 4-year Population-based Longitudinal Study (The Epifund Study)
Bibliographic record
Abstract
OBJECTIVE: To identify and characterize persons in the population who do not report musculoskeletal pain. METHODS: This was a population-based 4-year prospective longitudinal study by postal questionnaire. Population sample recruited from general practice registers in North-West England followed up at 15 months and 4 years. RESULTS: Of respondents, 17.4% [95% confidence interval (CI) 16.1%-19.7%] reported no pain in the previous month at all 3 measurement intervals over 4 years. They were characterized by low levels of psychological distress [relative risk (RR) low vs high levels of psychological distress 2.3; 95% CI 1.7-2.9], low levels of depression (2.7; 95% CI 2.0-3.6), low levels of anxiety (2.1; 95% CI 1.6-2.7), low health anxiety (1.6; 95% CI 1.2-2.1), and low illness behavior scores (5.8; 95% CI 4.0-8.3), good quality sleep (3.4; 95% CI 2.6-4.4), no somatic symptoms (RR 0 vs 3 or more, 3.1; 95% CI 1.6-6.3) and no adverse life events in the 6 months prior to baseline data collection (RR 0 vs 3 or more, 3.2; 95% CI 1.6-6.2). On multivariable analysis, good quality sleep, low illness behavior, low psychological distress, and absence of recent adverse life events remained statistically independent predictors of musculoskeletal health. In total, 46% of persons who had all 4 of these characteristics consistently reported being free of pain, compared to only 5% of those who had none. CONCLUSION: In a general population sample, over a period of 4 years, only around 1 in 6 persons do not report musculoskeletal pain. These persons report low levels of psychological distress and high quality sleep, both of which are potentially modifiable risk factors for the targeting of interventional or preventive strategies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".