The impact of shear force magnitude on cumulative injury load tolerance: a force weighting approach for low-back shear loads
Bibliographic record
Abstract
Vertebral joint fatigue life non-linearly decreases with increasing anterior shear force magnitude. Therefore, equal treatment of independent shear force exposures will underestimate low-back injury potential. This investigation developed mathematical functions for deriving appropriate weighting factors (WFs) to be applied in occupational cumulative shear force estimates. Porcine vertebral joints were repetitively loaded in shear to 20%, 40%, 60% or 80% of their calculated shear failure tolerance for 21,600 cycles or until bone failure was detected. Two WF functions were derived from mathematical relationships between sub-maximal shear force magnitude and sustained cumulative shear force to failure. These functions explained 98.2% and 88.6% of the variance in sustained cumulative shear force. Accelerated injury potential represented by WFs greater than unity was assigned to shear forces above 35.6% and 695 N. These weighting approaches will enhance sensitivity in future evaluations between exposure and lost-time/injury for detecting relationships between cumulative shear loading and low-back injury.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".