Cut-points to prevent low back injury due to force exertion at work
Bibliographic record
Abstract
Force exertion is related to low back injuries (LBI). This paper critically reviews the literature concerning cut-points for back force exertion, presents available guidelines in a concise manner, and identifies areas that need further research. The studies reviewed were grouped according to the criteria used to set the cut-point values. Most often cut-points differ than concur. The approach considering physiological, psychophysical, epidemiological, and biomechanical aspects of back force exertion meets the most known criteria and presents the lowest common denominator of instantaneous load for lifting tasks. Further experimental and epidemiological studies in peak load and cumulative exposure are necessary. Compound indices should also be developed for pushing, pulling, and carrying. Future indices should consider electromyographically determined fatigue, differential viscoelastic properties of tissues, aging, and the cross sectional area of back muscles. We hope that this paper contributes to a more systematic appraisal of back force exertion at work.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".