A New Slant on Shear Loading: Uncovering Its Effect on the Intervertebral Disc
Bibliographic record
Abstract
Prolonged anterior shear loading may contribute to disc degeneration by damaging the annulus fibrosus. To address this, annular mechanical properties were quantified following static shear loading using a porcine model. Twelve porcine cervical motion segments were dissected, with posterior bony elements removed to isolate shear to the intervertebral disc. Specimens were randomized into two conditions: (1) shear-loaded (100 N static anterior shear applied to C3/C4, n = 6) or (2) control (0 N, n = 6). Shear force was applied via a pin through C4, secured to a testing system to prevent rotation, while C3 was clamped such that anterior shear of C3 with respect to C4 resulted. Following 1 h of loading, two anterior annulus samples were extracted per specimen. The first sample underwent circumferential tensile testing, while the other was prepared for a peel test to assess interlamellar adhesion. Tensile properties in the circumferential direction remained unchanged after shear loading. However, interlamellar adhesive stiffness decreased by 52% (p = 0.02), and adhesive strength dropped by 46% (p = 0.02) in shear-loaded specimens compared to controls. Shear loading weakened the interlamellar matrix, reducing resistance to delamination and compromising disc integrity. These findings suggest that prolonged shear loading may contribute to early-stage disc damage.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".