Effect of <i>in vitro</i> testing over extended periods on the low‐load mechanical behaviour of dense connective tissues
Bibliographic record
Abstract
Many biomechanical studies are performed on dense connective tissues in the laboratory over a substantial time period; however, the effect of the in vitro testing environment on the cyclic load-relaxation behaviour of these structures is not well established. This study evaluated the effect of long duration of testing on ligament viscoelastic behaviour, using the rabbit femur-medial collateral ligament-tibia complex as a model. Dissected rabbit knee joints were mounted on a materials testing machine, and isolated ligament complexes were cycled at a frequency of one cycle/min to a fixed displacement of 0.7 mm for an 18-hour period. After an initial period of exponential load relaxation, the cyclic peak loads slowly decreased over the 18-hour period. The average decrease in the cyclic peak load between 0.5 and 18.0 hours was 0.26% (of the original peak load) per hour (r2 = 0.934), or a total of 8.6+/-4.6% over this period (p < 0.0001). Thus, low-load testing of dense connective tissues in the laboratory over extended periods significantly alters their biomechanical behaviour, and these changes should be considered in long-term laboratory-based studies of dense connective tissues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| 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".