The Influence of Enrichment Devices on Development of Osteoarthritis in a Surgically Induced Murine Model
Bibliographic record
Abstract
This study measured the influence of three different environmental enrichment devices (EEDs) on the severity of osteoarthritis (OA) in a surgically induced murine model. The development of OA requires joint movement after surgical instability induced by destabilization of the medial meniscus at 10 weeks of age. We evaluated the hypothesis that animals behavioral activity levels may influence the severity of the disease by investigating the effect of different EEDs on mouse activity and correlating this to OA severity. Thirty male 129S6/SvEvTac mice were housed in groups of five and provided with nesting material and one of three different EEDs: a heavy plastic tube (CPVC), Shepherd Shack (SS), or Tecniplast Mouse House (TMH). We videorecorded the cages throughout the study and constructed an ethogram. Eight weeks after surgery we euthanized the mice and performed a histologic examination of the knees to score the severity of OA based on the different housing systems, correlating the scores with behavioral activity levels for each cage. OA was higher in the mice with CPVC and TMH devices in their cages, whereas the mice with SS devices exhibited less cartilage damage; however, although we observed increased behavioral activity in mice with the CPVC tube and TMH and less in mice with the SS, the statistical results were not significant. The histological results of OA and the ethogram correlated to support our hypothesis that the type of EED plays an indirect role in the severity of the disease by modifying the activity levels of mice. In activity-dependent studies, the impact of an EED needs to be evaluated before change the environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".