Implementation issues of multivoxel STEAM‐localized <sup>1</sup>H spectroscopy
Bibliographic record
Abstract
Single-voxel STEAM-localized spectroscopy studies of neuropsychiatric patients yield high-quality data at short echo times, but are often limited to only a few regions of interest due to the linear increase of acquisition time with the number of regions examined. A multivoxel STEAM approach increases the number of regions of interest examined with a less than linear increase in acquisition time. Several implementation issues were considered, especially the signal contribution of outer voxel stimulated echoes (OVSE), which can lead to systematic errors in the quantification of relative metabolite concentrations. The relative signal contribution of OVSEs was found to be as great as 30% in phantoms. Gradient polarity switching completely canceled the contribution of OVSEs. A two-voxel STEAM approach produces phantom and in vivo data quality comparable to single-voxel STEAM in practically half the time. Quantification precision and accuracy are preserved in phantoms and in vivo.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".