Quantitative analysis of repaired rabbit supraspinatus tendons (± channeling) using magnetic resonance imaging at 7 Tesla
Bibliographic record
Abstract
BACKGROUND: The quantitative assessment of supraspinatus tendons by conventional magnetic resonance is limited by low contrast-to-noise ratio (CNR). Magnetic resonance imaging (MRI) scanners operating at 7 Tesla offer high signal-to noise ratio (SNR), low CNR and high spatial resolution that are well-suited for rapidly relaxing tissues like tendons. Few studies have applied T2 and T2* mapping to musculoskeletal imaging and to the rotator cuff tendons. Our objective was to analyze the T2 and T2* relaxation times from surgically repaired supraspinatus tendons and the effect of bone channeling. METHODS: One supraspinatus tendon of 112 adult female New Zealand white rabbits was surgically detached and repaired one week later. Rabbits were randomly assigned to channeling (n=64) or control (n=48) groups and harvested at 0, 1, 2, and 4 weeks. A 7T magnet was used for signal acquisition. For T2 mapping, a sagittal multi slice 2D multi-echo spin-echo (MESE) CPMG sequence with fat saturation was applied and T2* mapping was performed using a 3D UTE sequence. Magnetic resonance images from supraspinatus tendons were analyzed by two raters. Three regions of interest were manually drawn on the first T2-weighted dataset. For T2 and T2*, different ROI masks were generated to obtain relaxation times. RESULTS: 25.6±3.9 ms; P<0.05). T2 increased at 1, 2 and 4 postoperative weeks: 22.7±3.1, 23.3±3.9 and 24.0±5.1 ms, respectively, and values were significantly different from contralateral supraspinatus tendons (24.8±3.1; 26.8±4.3 and 26.5±3.6 ms; all P<0.05). Bone channeling did not affect T2 (P>0.05). CONCLUSIONS: Supraspinatus tendons detached for 1 week had shorter T2 relaxation time compared to contralateral as measured with 7T MRI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.000 | 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 teacher head, 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".