Applications of Proton MRS to Study Human Brain Metabolism
Bibliographic record
Abstract
Magnetic resonance spectroscopy (MRS) provides information that is rarely obtainable by other noninvasive means, or even by invasive methods using radioactive labels. For example, it provides the means to monitor in time and in space changes in various metabolic pools and allows one to think in terms of the biochemistry of these pools. In this sense, MRS is quite unique, and, although it cannot be said to be highly specific in diagnosing individual diseases, it nevertheless enables changes in many critical and characteristic parameters to be observed noninvasively for a broad range of metabolic abnormalities. By its nature MRS lends itself more toward the evaluation of diffuse brain diseases rather than that of focal lesions. For example, typical applications of MRS have been (1) to assess the regional distribution of neuronal dysfunction or death, (2) to evaluate distributions in the oxidative state of the brain, or (3) to detect regions of membrane abnormality. This list is growing as new MRS technology emerges. The adoption of MRS as a routine diagnostic and patient management tool in clinical medicine has, however, been quite slow when compared to the rapid acceptance of magnetic resonance imaging (MRI) several years ago. To understand this one must acknowl These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".