Identification of prevalence of musculoskeletal disorders and various risk factors in dentists
Bibliographic record
Abstract
Purpose: The awkward and repetitive movements lead to tissue straining, potentially leading to painful musculoskeletal disorders (MSDs). MSDs in dentists result in work inefficiency and a reduction in work hours. A survey was conducted to assess the prevalence of MSDs amongst the dental population of interest. Methods: Customized individual detail questionnaires, Standard Nordic Musculoskeletal questionnaires, and Level of Pain estimation using the Likert Scale were used to deduce the various responsible risk factors for the occurrence of MSDs in dentists. Inferential statistical analysis was done to identify the prevalence and severity of the MSDs. The Chi-Square test (95 % confidence interval) was used to identify and compare the association of risk factors involved in MSDs with the occurrence of the Effect of MSDs, the presence of MSDs, and the severity of the MSDs. Results: The results of the study deduced that the dentists followed the sedentary work practices. The dentists experienced maximum discomfort in the neck region, which was accompanied by the discomfort experienced in the lower back, hands and wrists, making the upper extremity being more susceptible to the MSDs. Gender risk factors the, the prevalence of MSDs in the dentist's upper back, and the severity of pain in the upper back region showed a significant association level. Conclusion: The wrist posture, the prevalence of MSDs and the severity of pain in the dentists' neck, shoulder and upper back showed a significant association level.
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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 | 0.002 |
| 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 | 0.000 |
| 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".