The effect of axial twist angle on <i>in vitro</i> cumulative injury load tolerance: a magnitude-weighting approach for axial twist exposures
Bibliographic record
Abstract
Axial twisting exposures have been repeatedly identified as a risk factor for occupational low back pain and injury, but there is a need for an improved understanding of the role of axial twist magnitude and associated joint moment as modifiers of the cumulative load tolerance of intervertebral joints. The purpose of this study was to mathematically characterise the relationship between axial twist motion magnitudes and the cumulative load tolerance of porcine cervical functional spinal units. Twenty-four porcine functional spinal units were fixed in a mechanical testing system under compressive load (15% of compressive tolerance) and in a neutral flexion-extension posture. Specimens were axially twisted to 5, 7.5, 10, 12.5, 15 or 17.5 degrees at 1 Hz until failure or 21,600 total cycles. Cumulative applied axial twist moment was recorded, and exponential functions were fit to the twist magnitude-cumulative twist moment measures. Weighting-factor functions for cumulative axial twist moment injury risk were developed based on absolute axial twist magnitude and twist normalised to maximum range of motion. While caution should be used in extrapolating these findings to human evaluation, a developed non-linear weighting-factor model has potential for use in assessment of cumulative axial twist injury risk in occupational tasks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".