Expanding the impact of the <scp>ISMRM</scp> young investigator awards: Introducing the <scp>Prince‐Meaney</scp> translational science award
Bibliographic record
Abstract
To the Editor: The Young Investigator Award (YIA) competition is a longstanding tradition of the International Society for Magnetic Resonance in Medicine (ISMRM). Bringing together some of the best science presented at the ISMRM Annual Meeting every year, the YIA session is a “cannot miss” event that features an exciting showcase of the brightest young members in the field. Historically, two awards have been given each year, including the I.I. Rabi Award awarded to the best work published in Magnetic Resonance in Medicine (MRM), describing a major engineering or scientific advance. The second major award, the W.S. Moore Award, is given to the best clinical science paper published in the Journal of Magnetic Resonance Imaging (JMRI). In many ways, this symmetry captures the essence of the incredible clinical, scientific, and engineering work performed by members of our Society. It is with great pleasure that we announce a significant ongoing financial gift to the ISMRM from Martin Prince, MD, PhD, and James Meaney, MD, two of our longstanding radiologist-scientist members, well known for their pioneering work in MR angiography over the last three decades. Through this support, we are pleased to announce that the ISMRM will expand the number of YIA Awards from two to three, with the introduction of the Prince-Meaney Translational Science Award, awarded to the best paper on translational science. This adds an important new dimension to the YIA portfolio, to celebrate translational work not well characterized by the Rabi or Moore Awards. It is with our deepest gratitude that we thank Professors Prince and Meaney for this remarkably generous support. The Prince-Meaney Award will be awarded for the first time at the Annual Meeting in Toronto this June to one of the finalist candidates of the YIA session, and we invite all of you to attend this session. We also invite all of our members in training to submit their best work to the YIA Program next year, including work in translational MR research. Finally, we extend our sincere thanks to members of the YIA Committee, past, present, and future, for their tremendous contributions in serving as stewards of the YIA program, the “Crown Jewel” of our Society.
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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 | 0.016 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.017 | 0.025 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".