Microstructural Damage in Arterial Tissue Exposed to Repeated Tensile Strains
Bibliographic record
Abstract
OBJECTIVES: Vertebral artery (VA) damage has been anecdotally linked to high-speed, low-amplitude spinal manipulative treatments (SMTs) of the neck. Apart from a single study quantifying the maximum stresses and strains imposed on the VA during cervical SMT, there are no data on the mechanics of the VA for this treatment modality, and there is no information on the possible long-term effects of repeat exposure to cervical SMT. The purpose of this study was to quantify microstructural damage in arterial tissue exposed to repeat strain loading of a magnitude similar to the maximum strains measured in the VA during high-speed, low-amplitude cervical SMT. METHODS: Twenty-four test specimens from cadaveric rabbit ascending aorta were divided into 2 control groups (n = 12) and 2 experimental groups (n = 6 each). Specimens were exposed to 1000 strain cycles of 0.06 and 0.30 of their in situ length. A pathologist, blinded to the experimental groups, assessed microstructural changes in the arteries using quantitative histology. Pearson chi(2) analysis (alpha = .05) was used to assess differences in tissue microstructure between groups. RESULTS: Control and 0.06 strain tissues were statistically the same (P = .406), whereas the 0.30 strain group showed microstructural damage beyond that seen in the control group (P = .024). CONCLUSIONS: We conclude that cadaveric rabbit arterial tissue similar in size and mechanical properties of that of the human VA can withstand repeat strains of magnitudes and rates similar to those measured in the cadaveric VA during cervical SMT without incurring microstructural damage beyond control levels.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".