Sodium imaging intensity increases with time after human ischemic stroke
Bibliographic record
Abstract
OBJECTIVE: Establishing time of onset is important in acute stroke management. Current imaging modalities do not allow determination of stroke onset time. Although correlations between sodium magnetic resonance imaging signal intensity within ischemic lesions and time of onset have been shown in animal models, the relation to onset time has not been established in human stroke. Utilizing high-quality sodium images, we tested the hypothesis that sodium signal intensity increases with time from symptom onset in human ischemic stroke. METHODS: Twenty-one stroke patients (63 +/- 15 years old) were scanned 4 to 104 hours after symptom onset. Follow-up images were obtained in 10 patients at 23 to 161 hours after onset, yielding a total of 32 time points. A standard stroke imaging protocol was acquired at 1.5 Tesla, followed by sodium magnetic resonance imaging at 4.7 Tesla. Relative sodium signal intensity within each lesion was measured with respect to the contralateral side. RESULTS: The sodium image quality was sufficient to visualize each acute lesion (lesion volume range, 1.7-217cm(3)). Relative sodium signal intensity increased nonlinearly over time after stroke onset. Sodium images acquired within 7 hours (n = 5) demonstrated a relative increase in lesion intensity of 10% or less, whereas the majority beyond 9 hours demonstrated increases of 23% or more, with an eventual leveling at 69 +/- 18%. INTERPRETATION: Increases of sodium signal intensity within the ischemic lesion are related to time after stroke onset. Thus, noninvasive imaging of sodium may be a novel metabolic biomarker related to stroke progression. Ann Neurol 2009;66:55-62.
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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 | 0.001 |
| 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) | 0.002 | 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".