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. Accurate assessment of the pain caused by these disorders is of paramount importance in its better control. Therefore, 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 the city of Tehran. Materials and Methods: In this cross-sectional descriptive-correlation study conducted among 120 dental students in Tehran in 2020-2021, data related to musculoskeletal disorders were collected through the Nordic questionnaire. Pain assessment was also performed using the Visual Analogue Scale and McGill Pain Questionnaire. The data were analyzed using SPSS version 26 and statistical tests, including chi-square, Spearman, and Pearson correlation, to examine the relationship between the two measures. Results: In this study, the highest prevalence of disorders was observed in the neck region (42.5%), upper back (35%), and lower back (31.7%). Neck pain and discomfort, in the last 12 months, caused a reduction in work activity more than any other region among the participants. Significant correlations were found between the Visual Pain Scale and the Sensory (r=0.543, p=0.011), Affective (r=0.549, p=0.020), and Evaluative (r=0.538, p=0.035) dimensions of the McGill Pain Questionnaire. Conclusion: The present study indicates that work-related musculoskeletal disorders are common in dental students. There is a high correlation between the Visual Pain Scale and the McGill Pain Questionnaire in assessing pain intensity.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".