Multisite evaluations of a <scp>T</scp><sub>2</sub>‐relaxation‐under‐spin‐tagging (<scp>TRUST</scp>) <scp>MRI</scp> technique to measure brain oxygenation
Bibliographic record
Abstract
PURPOSE: Venous oxygenation (Yv ) is an important index of brain physiology and may be indicative of brain diseases. A T2 -relaxation-under-spin-tagging (TRUST) MRI technique was recently developed to measure Yv . A multisite evaluation of this technique would be an important step toward broader availability and potential clinical utilizations of Yv measures. METHODS: TRUST MRI was performed on a total of 250 healthy subjects, 125 from the developer's site and 25 each from five other sites. All sites were equipped with a 3 Tesla (T) MRI of the same vendor. The estimated Yv and the standard error (SE) of the estimation εYv were compared across sites. RESULTS: The averaged Yv and εYv across six sites were 61.1% ± 1.4% and 1.3% ± 0.2%, respectively. Multivariate regression analysis showed that the estimated Yv was dependent on age (P = 0.009) but not on performance site. In contrast, the SE of the Yv estimation was site-dependent (P = 0.024) but was less than 1.5%. Further analysis revealed that εYv was positively associated with the amount of subject motion (P < 0.001) but negatively associated with blood signal intensity (P < 0.001). CONCLUSION: This work suggests that TRUST MRI can yield equivalent results of Yv estimation across different sites.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".