Bibliographic record
Abstract
Objective To analyze difficulties of temperature measuring by MR thermometry in different types of tissues near vertebra. Method Regions of interest( ROI) were segmented into several parts based on clinical needs and T1 / T2 maps were acquired in a 3T Phillips scanner. Proton Resonance Frequency( PRF) and spectrum estimation were used separately to measure the temperature in water-domain and water-fat mixed tissues, with field drift correction using muscles as background tissues. Results Eighty-three hundred pixels were chosen in each ROI for PRF method. The mean error value of the spinal cord and intervertebral disk was below 0.2 ℃, and the standard deviation was below 1.5 ℃. The mean error value of the aorta and vena cava was 0.9 ℃-1.8 ℃, while the standard deviation was below 1.7 ℃. The mean error value of the vertebra was around 0.9 ℃, while the standard deviation was near 12 ℃. The standard deviation of the vertebra water-fat mixed region was still above 12 ℃ by using spectrum estimation method. Conclusions MR thermometry nas good performances in regions of water-domain and less blood flow, while the blood flow in aorta and vena caca can induce disturbances in temperature measuring. Susceptibility caused by cancellous bone can also bring errors into temperature results.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".