Hand-Arm Vibration Syndrome in Dentistry: A Questionnaire Survey among Dentists and Review of Literature
Bibliographic record
Abstract
The use of dental handpieces and ultrasonic instruments expose dental professionals to high-frequency vibration, precise gripping, high pinch force, and repetitive bending movements of wrist during restorative procedures involving cutting dental material, periodontal scaling, and root planning. There is clear evidence of an association between the dentistry profession and work-related musculoskeletal disorders in the neck, upper back and upper extremities; however, the influence of high-frequency vibration on hand and fingers from dental handpieces is not well known. The objectives of the current paper are to present the results of a survey on hand-arm vibration syndrome (HAVS) among members of a professional dental society and to present a literature review on dental handpieces and ultrasonic scalers exposure assessment and occurrence of hand-arm vibration syndrome among dental professionals. There seems to be limited awareness of the occupational risk associated with hand-arm vibration from handpieces and ultrasonic devices. This study highlights the occurrence of vascular and neurological disorders of HAVS among dental professionals, as well as wrist/hand pain, osteoarthritis, diminished hand grip, and carpal tunnel syndrome. The assessment of high-frequency vibration and ultra-vibration from these vibrating tools and vibration-related injuries deserve special attention for future preventive measures.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".