Infarct Volume on Apparent Diffusion Coefficient Maps Correlates with Length of Stay and Outcome after Middle Cerebral Artery Stroke
Bibliographic record
Abstract
BACKGROUND: Diffusion-weighted MRI (DWI) can depict acute ischemia based on decreased apparent diffusion coefficient (ADC) values. ADC maps, unlike DWI (which have contributions from T2 properties), solely reflect diffusion properties. Recent studies indicate that severity of neurological deficit corresponds with degree of ADC alteration. PURPOSE: To determine whether infarct volume on ADC maps correlates with length of hospitalization and clinical outcome in patients with acute ischemic middle cerebral artery (MCA) stroke. STUDY POPULATION: Forty-five consecutive patients with acute ( 3 SDs below the average ADC value of a contralateral control region. Infarct volume was correlated with length of hospitalization and 6-month outcome assessed with Glasgow Outcome Scale (GOS), Modified Rankin Score (mRS), Barthel Index (BI) and a dichotomized outcome status with favorable outcome defined as GOS 1, mRS or=95. RESULTS: Infarct volume on ADC maps ranged from 0.2 to 187 cm(3) and was significantly correlated with length of hospitalization (p < 0.001, r = 0.67). Furthermore, ADC infarct volume was significantly correlated with GOS (r = 0.73), mRS (r = 0.68), BI (r = 0.67) and outcome status (r = 0.65) (each p < 0.001). Multiple logistic regression revealed a statistically significant correlation between ADC infarct volume and outcome status (p < 0.05), but none for Canadian Neurological Scale score, age and gender (p >0.05 each). CONCLUSION: Infarct volume measured by using a quantitative definition for infarcted tissue on ADC maps correlated significantly with length of hospitalization (as a possible surrogate marker for short-term outcome) and functional outcome after 6 months. ADC infarct volume may provide prognostic information for patients with acute ischemic MCA stroke.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".