Myotonometry in machinery operators and its relationship with postural ergonomic risk
Bibliographic record
Abstract
OBJECTIVES: To analyze the association between occupational ergonomic risk, personal characteristics, and working conditions with the biomechanical properties of stiffness and muscular tone in the paravertebral muscles of electric pallet jack and forklift operators in the industrial sector. METHODS: A total of 75 industrial sector machine operators were evaluated in 2021. Personal characteristics and working conditions were assessed through a questionnaire. Ergonomic risk was assessed using the Rapid Entire Body Assessment (REBA) method, and biomechanical properties of stiffness and muscular tone were obtained using the Myoton Pro device. Stiffness in paravertebral muscles was compared based on the operated machine and observed ergonomic risk. A multilevel linear regression model was employed to quantify the relationship, with mean differences and 95% CI calculated. RESULTS: Very high ergonomic risk was found in 75% of the electric pallet truck drivers. In this group with the highest ergonomic risk, an association between biomechanical properties and older workers was observed. Additionally, among electric pallet truck drivers, stiffness (mean difference 335.9 N/m, 95% CI: 46.4 (3.4 to 110.0), P < 0.05) and paravertebral muscle tone (mean difference 17.5 Hz, 95% CI: 1.4 (0.1 to 3.4), P < 0.05) showed statistically significant differences in the very high ergonomic risk category compared to the high-risk category. No significant differences were observed in any of the analyzed variables among forklift drivers. CONCLUSIONS: Workers operating electric pallet trucks with very high ergonomic risk according to the REBA method and aged over 40 yr are associated with increased muscle stiffness and tone.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".