Optimization of indirect arthrography of the knee by application of external heat: Initial experience
Bibliographic record
Abstract
PURPOSE: To examine the potential utility of applying heat to increase the uptake of intravenous gadolinium (Gd) contrast into the knee joint in order to optimize MR arthrography. MATERIALS AND METHODS: At 1.5T, 16 knees in eight patients without prior surgery, injury, or pain were examined before and 30 minutes after intravenous administration of Gd contrast (0.1 mM/kg). Between scans a heating pad was applied to the anterior aspect of eight randomly selected knees (the contralateral knee served as the control). Initial and postcontrast imaging consisted of identical axial T1-weighted sequences (TR/TE = 500/14 msec) without fat suppression. On the initial and postcontrast images, regions of interest (ROIs) were placed at identical locations in the suprapatellar pouch and the intercondylar notch by a reader blinded to the treated side. The values at these two locations were averaged and the change in joint signal intensity was calculated. The differences between the heated and unheated knees were also calculated. RESULTS: Seven of the eight knees treated with heat had increased joint enhancement compared to the contralateral control, with percentage changes in joint signal intensity (heated knee vs. control) of +38%, +80%, +121%, +145%, +150%, +164%, and +177%. Overall there was a doubling of signal intensity (125%) on the heated side compared to the contralateral control (with significance at P = 0.039). One patient was excluded because of a prior knee injury. CONCLUSION: The application of external heat increases uptake of intravenously administered Gd contrast into the knee joint, and may help to optimize indirect MR arthrography at a relatively low cost.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".