Measurement of articular cartilage surface irregularity in rat knee contracture.
Bibliographic record
Abstract
OBJECTIVE: To design novel quantitative methods to evaluate the irregularity of articular cartilage surface; and to apply these methods for assessment of surface irregularity in a rat knee contracture model. METHODS: A total of 117 rat knees were either immobilized or sham-operated and harvested after 2, 4, 8, 16, or 32 weeks, and 11 knees were not operated. Standardized histologic sections were digitized and the contours of femoral and tibial cartilage surfaces were delineated. The rates of change in cartilage contour were calculated. Rate of change above a defined threshold constituted surface irregularity. RESULTS: In non-operated knees, cartilage surface irregularity in femur and tibia amounted to 3.1 +/- 0.5%. Immobilized knees showed significantly more irregularities than the sham-operated knees at all time points (2 weeks: 5.3 +/- 0.6% vs 3.1 +/- 0.4%; 4 weeks: 10.5 +/- 0.9% vs 4.4 +/- 0.9%; 8 weeks: 12.0 +/- 1.8% vs 4.9 +/- 0.2%; 16 weeks: 13.7 +/- 2.0% vs 4.9 +/- 0.4%; and 32 weeks: 13.8 +/- 1.4% vs 3.4 +/- 0.6%; all p < 0.05). No difference was observed between sham-operated and non-operated knees. Increasing duration of immobilization in weeks (t) significantly correlated with more surface irregularity, described by the logarithmic formula: % irregularity = 6.6 + 2.1 ln (t), (F = 59.3, p < 0.001). This formula showed that irregularity progressed rapidly after immobilization and plateaued after 8 weeks. CONCLUSION: We designed methods to quantify cartilage surface irregularity and applied them to a contracture model. Cartilage surface irregularities appeared after 2 weeks of immobilization and progressed rapidly to plateau after 8 weeks. Combined with microscopic magnetic resonance imaging, this measurement of cartilage surface irregularity may constitute a sensitive tool to detect cartilage degeneration clinically.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".