Identifying lesion growth with MR imaging in acute ischemic stroke
Bibliographic record
Abstract
PURPOSE: To determine whether different MR diffusion- and perfusion-weighted imaging (DWI and PWI) parameters are important in distinguishing lesion growth from the acute lesion and from oligemia. MATERIALS AND METHODS: MR DWI and PWI were acquired from thirteen patients. We defined three regions: (i) LESION - intersection of acute and final lesions, (ii) GROWTH - portion of final lesion not part of acute lesion, and (iii) OLIGEMIA - region of perfusion abnormality not part of either the acute or final lesions. We used logistic regression modeling to distinguish GROWTH from LESION and from OLIGEMIA on a voxel-wise basis using DWI- and PWI-based parameters. Final models were selected based on the Wald statistic and validated by cross-validation using the mean (+/- standard deviation) area under the curve (AUC) from receiver operating characteristic analysis. RESULTS: The final model for differentiating GROWTH from LESION included DWI, the apparent diffusion coefficient (ADC), cerebral blood flow (CBF) and tissue type (AUC = 0.939 +/- 0.028). The final model for differentiating GROWTH from OLIGEMIA included DWI, ADC, CBF, and time-to-peak (AUC = 0.793 +/- 0.106). CONCLUSION: Different MR parameters are important in differentiating lesion growth from acute lesion and from oligemia in acute ischemic 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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".