Exercise‐Induced Changes in Knee Cartilage In Vivo: Comparing MRI Sequences
Bibliographic record
Abstract
The purpose was to assess the agreement in measures of acute knee cartilage thickness and composition change after loading in clinical knee osteoarthritis (OA) between two magnetic resonance imaging (MRI) acquisition approaches: (1) single sequence approach using quantitative double-echo in steady-state (qDESS), which allows simultaneous morphological and compositional scanning, versus (2) multi-sequence approach that captures morphology (fast spoiled gradient recalled (FSPGR) or qDESS) and composition (multi-echo spin echo (MESE)) separately. Twenty adults with clinical knee OA participated. 3T MR scans were acquired before and immediately after a 25-min treadmill walk at a standardized speed. Changes in knee cartilage thickness and T2 were assessed. Pre-activity, strong agreement was observed in cartilage thickness captured with qDESS and FSPGR (concordant correlation coefficients 0.842-0.935). Pre-activity, we observed greater absolute cartilage thickness with qDESS compared to FSPGR in femoral cartilage. From pre- to post-activity, qDESS showed change in cartilage thickness in the medial femur (-0.088 ± 0.11 mm, p = 0.002), lateral tibia (-0.042 ± 0.65 mm, p = 0.011) and trochlea (-0.027 ± 0.05 mm, p = 0.024); whereas FSPGR showed a change only in the lateral tibia (-0.064 ± 0.08 mm, p = 0.002). From pre- to post-activity, qDESS showed reduced T2 in all cartilage regions; whereas qDESS + MESE and FSPGR + MESE detected T2 changes in the patella (-1.90 ± 3.00 ms, p = 0.013, and -1.80 ± 2.18 ms, p = 0.002, respectively). qDESS detects transient changes in knee cartilage due to loading in clinical knee OA.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".