Consensus recommendations for hyperpolarized [1‐ <scp> <sup>13</sup> C </scp> ]pyruvate <scp>MRI</scp> multi‐center human studies
Bibliographic record
Abstract
Abstract MRI of hyperpolarized (HP) [1‐ 13 C]pyruvate allows in vivo assessment of metabolism and has translated into human studies across diseases at 15 centers worldwide. To determine consensus on best practice for multi‐center studies for development of clinical applications. This paper presents the results of a two‐round formal consensus building exercise carried out by experts with HP [1‐ 13 C]pyruvate human study experience. Twenty‐nine participants from 13 sites brought together expertise in pharmacy methods, MR physics, translational imaging, and data analysis with the goal of providing recommendations and best practice statements on conduct of multi‐center human studies of HP [1‐ 13 C]pyruvate MRI. Overall, the group reached consensus on approximately two‐thirds of 246 statements in the questionnaire, covering HP 13 C‐pyruvate preparation; MRI system setup, calibration, and phantoms; acquisition and reconstruction; and data analysis and quantification. Consensus was present across categories. Examples include: (i) Different HP pyruvate preparation methods could be used in human studies, but the same release criteria have to be followed; (ii) site qualification and quality assurance must be performed with phantoms and the same field strength must be used, but the rest of the system setup and calibration methods could be determined by individual sites; (iii) the same pulse sequence and reconstruction methods were preferable, but the exact choice should be governed by the anatomical target; (iv) normalized metabolite area‐under‐curve values and metabolite area under curve were the preferred metabolism metrics. The consensus proces revealed that HP[1‐ 13 C] pyruvate MRI as a technology has progressed sufficiently to plan multi‐center studies. The work confirmed areas of consensus for multi‐center study conduct and identified where further research is required to ascertain best practice.
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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 | 0.522 | 0.546 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.019 | 0.020 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".