Construct and Predictive Validity of the Chronic Pain Grade in Workers With Chronic Work-related Upper-extremity Disorders
Bibliographic record
Abstract
OBJECTIVE: To evaluate the ability of Chronic Pain Grade (CPG) questionnaire to predict upper-extremity physical disability, at-work disability, and work status in workers with chronic work-related upper-limb injuries. METHODS: A total of 448 individuals with chronic work-related injuries were assessed at baseline and 6 months later. At each evaluation, 4 self-reported questionnaires were completed (CPG, QuickDASH, Work Limitations Questionnaire, and Work Instability Scale), and current work status was evaluated. Predictive validity of CPG was evaluated using proportion tests. RESULTS: At baseline, 5% of participants had a CPG at Grade I, 7% at Grade II, 18% at Grade III, and 70% at Grade IV (high disability-severely limiting). Twenty-six percent of workers transitioned in terms of their work status (7% from not working to working, 19% working to not working). Higher Grades on CPG at baseline could not predict improvement or deterioration 6 months after for upper-extremity disability (QuickDASH), at-work productivity loss (Work Limitations Questionnaire), or work instability (Work Instability Scale). Initial CPG could predict 6-month work status in the full sample. However, when considering only participants not working at baseline, CPG did not predict return to work. DISCUSSION: CPG has low to moderate ability to predict 6-month work status in patients with chronic upper-extremity disorders. Both a lack of CPG and work transition variability may have contributed to this finding. Extension of the upper end of CPG range might be investigated as a means to increase discrimination at the upper end spectrum of chronic pain, which predominate the population of patients with chronic musculoskeletal disorders.
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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 | 0.012 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".