Imaging of the rabbit supraspinatus enthesis at 7 Tesla: a 4‐week time course after repair surgery and effect of channeling
Bibliographic record
Abstract
PURPOSE: To image the supraspinatus enthesis reformation of rabbit shoulders by magnetic resonance at 7 Tesla (T) using T2 mapping after surgical repair and to assess the effects of channeling aimed at enhancing enthesis reformation. MATERIALS AND METHODS: In 112 rabbits, the distal supraspinatus (SSP) tendon was unilaterally detached and reattached after 1 week. At the first surgery, channeling was performed at the footprint in 64 rabbits. At the second surgery, the SSP tendon of all rabbits was re-attached to the greater tuberosity. The shoulders were harvested at 0, 1, 2, or 4 weeks after the repair surgery and were imaged at 7T. Quantitative T2 mapping was performed using multi slice two-dimensional multi-echo spin-echo sequence with fat saturation. Enthesis regions of interests were drawn on three slices at the footprint to measure T2 relaxation times. RESULTS: = 4.8; P = 0.006) both affected significantly the T2 values while channeling had no significant effect. For the time effect, the only pair with a statistical difference was the 0-week and 4-week for the channeling groups (P = 0.023). CONCLUSION: Enthesis reformation early after surgical repair of the SSP distal tendon was characterized by increasing T2 values. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:461-467.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".