Use of pre‐clinical surgically induced models to understand biomechanical and biological consequences of PTOA development
Bibliographic record
Abstract
Post-traumatic osteoarthritis (PTOA) development is often observed following traumatic knee injuries involving key stabilising structures such as the cruciate ligaments or the menisci. Both biomechanical and biological alterations that follow knee injuries have been implicated in PTOA development, although it has not been possible to differentiate clearly between the two causal factors. This review critically examines the outcomes from pre-clinical lapine and ovine injury models arising in the authors' laboratories and differing in severity of PTOA development and progression. Specifically, we focus on how varying severity of knee injuries influence the subsequent alterations in kinematics, kinetics, and biological outcomes. The immediate impact of injury on the lubrication capacity of the joint is examined in the context of its influence on biomechanical alterations, thus linking the biological changes to abnormal kinematics, leading to a focus on the potential areas for interventions to inhibit or prevent development of the disease. We believe that PTOA results from altered cartilage surface interactions where biological and biomechanical factors intersect, and mitigating acute joint inflammation may be critical to prolonging PTOA development. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:454-465, 2017.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".