Indirect Injury Stimulates Scar Formation–Adaptation or Pathology?
Bibliographic record
Abstract
In several animal models of osteoarthritis induced by cruciate ligament transection, a dense, scar-like tissue mass forms rapidly on the medial side of the knee joint. This mass mimics clinical fibrosis that sometimes occurs after joint surgery. It is unknown exactly why this medial tissue mass forms and what cells are involved in its formation. This study characterizes this medial mass by histology, biochemistry, and the expression of types I and III collagen mRNA. The medial mass is compared with the medial collateral ligament (MCL) and the MCL epiligament in anterior cruciate-transected and unoperated joints, and to normal skin and skin scar. The morphology of the medial mass resembled the epiligament and skin scar more than the MCL. The concentration of DNA and RNA and the RNA-DNA ratio were elevated dramatically in the medial mass compared with all other tissues including skin scar. However, the mRNA copy number and ratio of collagen types I and III mRNAs did not differ significantly among the medial mass, MCL, epiligament, and skin in either the control or the operated joints. The response of the medial mass, MCL, and MCL epiligament to cruciate transaction involves both hyperplasia and hypertrophy, but without a dramatic shift in cell phenotype. The medial mass may be a useful mimic or model of intraarticular adhesions, hypertrophic scars, ligament sprains, and arthrofibrosis.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".