Bibliographic record
Abstract
High signal-to-noise ratio (SNR) and the ability to acquire high-resolution thin section images are major advantages of 3 Tesla (T) that benefit musculoskeletal (MSK) imaging. Use of 3 T for pediatric MSK imaging is still in its early phase, and actual clinical benefits are not yet clear. However, initial reports in adult and our experience suggest that 3 T is better in imaging cartilage and small joints. It provides good quality images even for small field of views, which is advantageous in children. It shows cartilage, ligaments, and nerves better. After optimization, overall examination time is shorter at 3 T, which has the potential to reduce the need for sedation and increase throughput. 3-T imaging has the potential to improve small lesion evaluation and tumor staging, and it can be used for whole-body screening for metastasis. We discuss the technical differences, artifacts, and safety issues of 3 T, followed by our initial clinical experience with illustrative examples in pediatric MSK imaging at 3 T.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".