Validation of the Hanyang Pain Scale for clerical workers with musculoskeletal pain
Bibliographic record
Abstract
PURPOSE: The visual analog scale (VAS) is the most widely used scale for pain assessment. However, its reflection of time-, sleep-, work-, psychological-, and reward-related pain characteristics is limited. Therefore, this study aimed to develop a new pain scale, the Hanyang Pain Scale (HPS), evaluate its reliability, and assess its agreement with currently used scales. SUBJECTS AND METHODS: The HPS comprises a 10 cm long visual vertical bar, similar to the VAS, with eleven simple evaluation sentences related to pain frequency, work, and sleep. We selected 1,037 clerical workers as study subjects and conducted medical examinations through interviews, physical examinations, and musculoskeletal pain assessments tools including the VAS, HPS, and McGill pain questionnaire (MPQ). The reliability of the HPS and its agreement with VAS and MPQ were statistically analyzed. RESULTS: HPS test-retest reliability was very high (Pearson correlation coefficient =0.902). In particular, HPS test-retest reliability in the weak pain group (<4 points for both VAS and HPS) was greater (Pearson correlation coefficient =0.863) than that of VAS (0.721). Therefore, the HPS showed consistent pain assessment results in cases of relatively weak pain. Correlation was high between HPS and VAS scores (Spearman's ρ =0.526) and satisfactory between HPS and MPQ scores (Spearman's ρ =0.367). CONCLUSION: The newly developed HPS has high reliability and strong agreement with other currently widely used scales. In particular, HPS was more consistent than the VAS for relatively weak pain. Based on these findings, the HPS can be considered a useful pain assessment tool for clerical workers. Further clinical research on musculoskeletal diseases and on workers in other fields is required.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".