Murine Axial Compression Tibial Loading Model to Study Bone Mechanobiology: Implementing the Model and Reporting Results
Bibliographic record
Abstract
In vivo, tibial loading in mice is increasingly used to study bone adaptation and mechanotransduction. To achieve standardized and defined experimental conditions, loading parameters and animal-related factors must be considered when performing in vivo loading studies. In this review, we discuss these loading and animal-related experimental conditions, present methods to assess bone adaptation, and suggest reporting guidelines. This review originated from presentations by each of the authors at the workshop "Developing Best Practices for Mouse Models of In Vivo Loading" during the Preclinical Models Section at the Orthopaedic Research Society Annual Meeting, San Diego, CA, March 2017. Following the meeting, the authors engaged in detailed discussions with consideration of relevant literature. The guidelines and recommendations in this review are provided to help researchers perform in vivo loading experiments in mice, and thus further our knowledge of bone adaptation and the mechanisms involved in mechanotransduction. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 38:233-252, 2020.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".