Strain Response in the Facet Joint Capsule During Physiological Joint Rotation and Translation Following a Simulated Impact Exposure: An In Vitro Porcine Model
Bibliographic record
Abstract
Low back pain (LBP) is frequently reported following rear impact collisions. Knowledge of how the facet joint capsule (FJC) mechanically behaves before and after rear impact collisions may help explain LBP development despite negative radiographic evidence of gross tissue failure. This study quantified the Green strain tensor in the facet joint capsule during rotation and translation range-of-motion tests completed before and following an in vitro simulation of a rear impact collision. Eight FSUs (4 C3-C4, 4 C5-C6) were tested. Following a preload test, FSUs were flexed and extended at 0.5 deg/s until an ±8 N·m moment was achieved. Anterior and posterior joint translation was then applied at 0.2 mm/s until a target ±400 N shear load was imposed. Markers were drawn on the facet capsule surface and their coordinates were tracked during pre- and postimpact range-of-motion tests. Strain was defined as the change in point configuration relative to the determined neutral joint posture. There were no significant differences (p > 0.05) observed in all calculated FJC strain components in rotation and translation before and after the simulated impact. Our results suggest that LBP development resulting from the initiation of strain-induced mechanoreceptors and nociceptors with the facet joint capsule is unlikely following a severe rear impact collision within the boundaries of physiological joint motion.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".