Investigation of musculoskeletal disorders prevalence and the correlation of Visual Analog Scale with McGill Pain Questionnaire in dental students of Tehran universities
Bibliographic record
Abstract
Introduction: Work-related musculoskeletal disorders are one of the most common occupational complaints among dentists. This study aims to investigate the prevalence of musculoskeletal disorders and evaluate the correlation of the Visual Analogue Scale with the different dimensions of the McGill Pain Questionnaire in dental students in Tehran. Understanding this correlation leads to improved pain control by increasing the effectiveness of pain assessment. Materials and Methods: This cross-sectional descriptive-correlation study was conducted among 120 dental students in Tehran in 2020-2021. Data related to musculoskeletal disorders were collected through a Nordic questionnaire. Pain assessment was done using the Visual Analogue Scale and McGill Pain Questionnaire. The data were analyzed using SPSS version 26 software and chi-square, Spearman, and Pearson correlation tests to check the relationship between the two scales. Results: In this study, the highest prevalence of disorders was observed in the neck area (42.5%), upper back (35%), and waist (31.7%). Discomfort and pain in the neck in the last 12 months caused a decrease in work activity among the participants more than any other area. A significant correlation was observed between the visual pain scale and the sensory, affective, and evaluative pain perception group of the McGill Pain Questionnaire. Conclusion: The present study shows that work-related musculoskeletal disorders are common in dental students. It is recommended that corrective and educational measures using ergonomic science be on the agenda.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".