Absolute metabolite concentrations calibrated using the total water signal in brain <sup>1</sup>H MRS
Bibliographic record
Abstract
Magnetic resonance spectroscopy (MRS) has been coupled with a multi-echo imaging sequence to determine the relaxation corrected signal areas of the metabolites and the tissue water. Stimulated echo acquisition mode (STEAM) spectra (TE/TM/TR 30/13.7/5000 ms) acquired from gray and white matter voxels in 43 healthy volunteers were fit using LCModel. Corresponding water signals, measured using a multi-echo T(2) imaging sequence, were fit with a Non-Negative Least Squares algorithm. Using this approach the water area could be T(1) and T(2) corrected for all three water compartments: cerebrospinal fluid (CSF), intra- and extra-cellular water, and myelin water. The image-based water measurement is an improvement over spectroscopy methods because it can be more sensitive to water changes in diseased tissue. Metabolite areas were also corrected for relaxation losses. In occipital gray matter, the concentrations of Cho, Cr, and N-acetyl aspartate (NAA) were 1.27 (0.06), 8.9 (0.3), and 9.3 (0.3) mmol/L tissue, respectively and in parietal white matter they were 1.90 (0.05), 7.9 (0.2), and 9.8 (0.2) mmol/L tissue. The Cho and Cr concentrations were different in occipital gray compared to parietal white matter (p < 0.0001 and <0.005, respectively).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".