Accuracy and reliability of MRI vs. laboratory measurements in an ex vivo porcine model of arthritic cartilage loss
Bibliographic record
Abstract
PURPOSE: To quantify the accuracy of magnetic resonance imaging (MRI) measurement of change in cartilage volume due to thin linear excisions, simulating arthritic cartilage losses, by comparison with laboratory volume measurements in an ex vivo porcine model. MATERIALS AND METHODS: We scanned 15 porcine patellae by T1-weighted spoiled gradient echo (SPGR) MRI at baseline and after excision of up to three thin layers of articular cartilage. Excised fragment volume was determined from density and weight. Postexcision scans were "fused" to the baseline scan by three-dimensional (3D) registration. This allowed automated recalculation of the remaining cartilage volume within a baseline region of interest (ROI) following each excision. We compared MRI estimates of change in cartilage volume to direct laboratory measurement of fragment volume. RESULTS: Our 38 excised fragments averaged 0.16 mL, or approximately 7% of cartilage volume. MRI and laboratory estimates of total cartilage volume loss differed by 1.6% +/- 13.2% (mean, coefficient of variation [CV]). Accuracy was +/-0.1 mL for 95% of scans. CONCLUSION: MRI estimates of small changes in porcine patellar cartilage volume were unbiased, reliable, and accurate to 0.1 mL. Despite a proportionately high error in the very thin fragments tested, achievement of similar accuracy in vivo would be adequate to detect approximately two years of osteoarthritic cartilage loss.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".