Messenger Ribonucleic Acid Levels in Disrupted Human Anterior Cruciate Ligaments
Bibliographic record
Abstract
Thirty patients had anterior cruciate ligament reconstruction for ongoing instability. Two groups were defined according to gross morphologic features identified during reconstruction: anterior cruciate ligament disruptions with scars attached to a structure in the joint and disruptions without reattachments. Reverse transcription polymerase chain reaction for a subset of extracellular matrix molecules, proteinases, and proteinase inhibitors was done on samples of scarred anterior cruciate ligament tissue removed during reconstructive surgery. Results of the nonattached scar group showed significantly increased mRNA levels for Type I collagen, and an increased Type I to Type III collagen ratio compared with that for the attached scar group. In the first year after injury, decorin mRNA levels in the nonattached scar group also were significantly higher than in the attached scar group. Biglycan mRNA levels in the nonattached scar group correlated closely with Type I collagen mRNA levels. These results suggest differences in cellular expression in torn anterior cruciate ligaments that attach to structures in the joint versus those which do not. Although the molecular mechanisms responsible for these differences have not been delineated, different molecular signals may influence the gross morphologic features of anterior cruciate ligament disruptions or alternatively, differing gross morphologic features may be subject to different mechanical loads leading to altered molecular expression. However, the finding of endogenous cellular activity in injured anterior cruciate ligaments raises the possibility that this activity may be enhanced to improve outcomes.
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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 | 0.001 |
| 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) | 0.001 | 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".