Assessment of the Precision in Measuring Glutathione at <scp>3 T</scp> With a <scp>MEGA‐PRESS</scp> Sequence in Primary Motor Cortex and Occipital Cortex
Bibliographic record
Abstract
BACKGROUND: H spectroscopy techniques within a range of applications in healthy volunteers and disease states. Good test-retest reproducibility is key when assessing the efficacy of treatments aimed at modulating GSH levels within the central nervous system or when noninvasively assessing changes in GSH content over time. PURPOSE: To evaluate the intraday (in vitro and in vivo) and 1-month apart (in vivo) test-retest reproducibility of GSH measurements from GSH-edited MEGA-PRESS acquisitions at 3 T in a phantom and in the brain of a cohort of middle-aged and older healthy volunteers. STUDY TYPE: Prospective. SUBJECTS/PHANTOMS: A phantom containing physiological concentrations of GSH and metabolites with overlapping spectral signatures and 10 healthy volunteers (4 F, 6 M, 55 ± 14 years old). FIELD STRENGTH/SEQUENCE: GSH-edited spectra were acquired at 3 T using the MEGA-PRESS sequence. ASSESSMENT: The phantom was scanned twice and the healthy subjects were scanned three times (on two separate days, 1 month apart). GSH was quantified from each acquisition, with the in vivo voxels placed at the primary motor cortex (PMC) and the occipital cortex (OCC). STATISTICAL TESTS: Mean coefficients of variation (CV) were used to assess short-term (in vitro and in vivo) and longer-term (in vivo) test-retest reproducibility. RESULTS: In vitro, the CV was 2.3%. In vivo, the mean intraday CV was 3.3% in the PMC and 2.4% in the OCC, while the CVs at 1 month apart were 4.6% in the PMC and 7.8% in the OCC. DATA CONCLUSION: GSH-edited MEGA-PRESS spectroscopy allows measurement of GSH with excellent precision. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".