Combined <sup>1</sup> H MR spectroscopy and diffusion-weighted MRI improves the prediction of stroke outcome
Bibliographic record
Abstract
BACKGROUND: The prognostic value of the biochemical changes seen with proton MR spectroscopy (1H MRS) in ischemic stroke was examined. Acute diffusion-weighted imaging (DWI) was used to identify regions of ischemia for 1H MRS voxel localization. METHODS: Nineteen patients had 36 1H MRS studies, 13 patients acutely (mean, 11.1 hours), 10 subacutely (mean, 3.9 days), and 13 at outcome (mean, 82 days). Single-voxel, long-echo, timepoint-resolved spectroscopy was used to obtain lactate, n-acetylaspartate (NAA), choline, and creatine levels from the infarct core. Outcome measures were final infarct volume and clinical assessment scales (Canadian Neurological Scale, Barthel Index, and Rankin Scale). RESULTS: Acute lactate/choline ratio correlated more strongly with clinical outcome scores (r = 0.76 to 0.83; p < 0.01) and final infarct size (r = 0. 96; p < 0.01) than acute DWI lesion volume or acute NAA/choline ratio. Combination of acute lactate/choline ratio with acute DWI lesion volume improved prediction of all outcome scores (R2 = 0.80 to 0.90). The predictive effect of acute lactate/choline ratio was independent of acute DWI lesion volume (p < 0.001). In subacute and chronic infarction, both lactate/choline and NAA/choline ratios continued to correlate with outcome (p < 0.05). At the chronic stage, persistent lactate/choline ratio elevation strongly correlated with outcome measures (r = 0.71 to 0.87). CONCLUSION: Lactate/choline ratio measured in the acute infarct core by 1H MRS improves the prediction of stroke outcome and provides prognostic information complementary to DWI. Lactate/choline ratio could be used as an additional marker to select patients for acute and chronic therapies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".