Exogenous transforming growth factor beta 1 alone does not improve early healing of medial collateral ligament in rabbits.
Bibliographic record
Abstract
OBJECTIVE: To determine whether transforming growth factor beta 1 (TGF-beta1) improves early ligament healing. DESIGN: Experimental, controlled study of medial collateral ligaments (MCLs) in rabbits' knees. SETTING: Research laboratory. SUBJECTS: Sixteen skeletally mature, New Zealand White female rabbits. INTERVENTIONS: Ten rabbits had a standardized gap injury made in the MCL of both knees. Three weeks later, a second operation was performed to inject 7 microg of TGF-beta1 in a carrier solution into the right knee MCL, while the left knee MCL was injected with carrier alone. The rabbits were killed 3 weeks after the injection of TGF-beta1 (6 weeks after the original injury). Six of the rabbits (12 knees) had no operation on the MCL and served as external normal controls. OUTCOME MEASURES: Biomechanical measures of the femur-MCL-tibia complex. Histologic evaluation of MCL cell and matrix organization. Transmission electron microscopy measures of MCL fibril diameters. RESULTS: There were no statistically significant differences in the biomechanical measures, fibril diameter distributions and histologic evaluation of the injured MCLs treated with TGF-beta1 or carrier alone. Both groups of injured MCLs were significantly different from normal MCLs. CONCLUSIONS: The results indicate that the dosage and route of delivery of TGF-beta1 did not lead to overt improvement in the healing of the injured MCL. Whether different doses or delivery methods, alone or in combination with TGF-beta1, or other growth factors would lead to improvement remains to be determined.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".