Modeling and Staging of Osteoarthritis Progression Using Serial CT Imaging and Arthroscopy
Bibliographic record
Abstract
Objective The objective of this study was to describe in life methods by which osteoarthritis can be staged in order to time therapeutic interventions that are relevant to osteoarthritis (OA) clinical trials Methods Twenty-two sheep underwent arthroscopic meniscal destabilization to induce OA. Serial computed tomography (CT) imaging and arthroscopy were used to monitor osteoarthritis progression at 3-month intervals over 9 months. Eleven sheep received 1 intra-articular injection of hyaluronate 3 months after OA induction and another group of 11 received saline. A linear mixed model was used to define the trajectory of shape change in the medial joint compartment. Ordinal logistic regression was used to investigate the association between morphological changes and sclerosis. Results Three months after meniscal destabilization there were early bipolar chondral lesions in the medial compartment of the knee, as well as osteophytes and bone remodeling. Superficial fissures and cartilage cracks progressed to discrete areas of cartilage thinning and fibrillation on the medial tibial plateau by 6 months that became cartilage erosions by nine months. A linear mixed effect model demonstrated significant change in medial compartment length and width with over time ( P < 0.05) for both groups. A significant association between severity of sclerosis and medial compartment morphology was also observed. Conclusions The induction of osteoarthritic lesions with meniscal release model can be followed using noninvasive and minimally invasive procedures allowing for real-time decisions about redosing therapies, or other changes such as extending trial timelines without sacrificing animals to conduct assessments.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".