Imaging of glutamate in acute traumatic brain injury using chemical exchange saturation transfer
Bibliographic record
Abstract
BACKGROUND: Chemical exchange saturation transfer (CEST) is an important contrast mechanism in the field of magnetic resonance imaging. Herein, we used CEST for glutamate (GluCEST) imaging to evaluate the Glu alterations in acute mild to moderate traumatic brain injury (TBI) and correlated such alterations with the cognitive outcome at 1-month postinjury. METHODS: Thirty-two patients with well-documented mild-to-moderate TBI and 15 healthy controls (HC group) underwent 3.0-Tesla magnetic resonance imaging (MRI) with GluCEST, and magnetic resonance spectroscopy (MRS) scans. The Montreal Cognitive Assessment (MoCA) examination was administered to all study subjects at 1-month postinjury for cognitive outcome acquisition and divided TBI patients into patients with good cognitive outcome (GCO group) and with poor cognitive outcome (PCO group). RESULTS: The GluCEST% values for the occipital gray matter (OGM) and bilateral parietooccipital white matter (PWM) were higher in the PCO group compared with the HC and GCO groups (P<0.05), whereas the GluCEST% value showed no significant differences between the GCO and HC groups (P>0.05). In comparison with HCs, TBI patients had a significantly increased GluCEST% value for the OGM and bilateral PWM (P<0.05). GluCEST performed better than MRS in the prediction of cognitive outcome for TBI patients (P<0.05). CONCLUSIONS: Glu is significantly increased in acute TBI and strongly correlates with the cognitive outcome at 1month postinjury. GluCEST may supply new insight into TBI and help to improve the accuracy of short-term outcome prediction.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 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".