Does Annulus Fibrosus Lamellar Adhesion Testing Require Preconditioning?
Bibliographic record
Abstract
The interlamellar matrix (ILM), located between the annular layers of the intervertebral disc (IVD), is an adhesive component which acts to resist delamination. Investigating the mechanical properties of the ILM can provide us with valuable information regarding risk of disc injury; however given its viscoelastic nature, it may be necessary to conduct preconditioning on tissue samples before measuring these ILM properties. Therefore, the aim of this study was to optimize mechanical testing protocols of the ILM by examining the effect of preconditioning on stiffness and strength of this adhesive matrix. Eighty-eight annular samples were dissected from 22 porcine cervical discs and randomized into one of four testing conditions consisting of ten cycles of 15% strain followed by a 180 deg adhesive peel test. The four testing groups employed a different strain rate for the ten cycles of preconditioning: 0.01 mm/s (n = 23); 0.1 mm/s (n = 26); 1 mm/s (n = 23); and no preconditioning employed (n = 16). Samples preconditioned at 0.01 mm/s were significantly less stiff than those that had not received preconditioning (p = 0.014). No other results were found to be statistically significant. Given the lack of differences observed in this study, preconditioning is likely not necessary prior to conducting a 180 deg peel test. However, if preconditioning is employed, the findings from this study suggest avoiding preconditioning conducted at very slow rates (i.e., 0.01 mm/s) as the long testing time may negatively affect the tissue.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".