Final infarct volume estimation on 1-week follow-up MR imaging is feasible and is dependent on recanalization status
Bibliographic record
Abstract
PURPOSE: We aim to characterize infarct volume evolution within the first month post-ischemic stroke and to determine the effect of recanalization status on early infarct volume estimation. METHODS: Ischemic stroke patients recruited for the MONITOR and VISION studies were retrospectively screened and patients who had infarcts on diffusion-weighted imaging (DWI) at baseline and had at least two follow-up MR scans (n = 56) were included. Pre-defined target imaging time points, obtained on a 3-T MR scanner, were 12 hours (h), 24 h, 7 days, and ≥30 days post-stroke. Infarct tissue was manually traced blinded to the images at the other time points. Infarct expansion index was calculated by dividing infarct volume at each follow-up time point by the baseline DWI infarct volume. Recanalization was assessed within 24 h post-stroke. Correlation and statistical comparison analysis were done using the Spearman, Mann-Whitney, and Kruskal-Wallis tests. RESULTS: Follow-up infarct volumes were positively correlated with the baseline infarct volume (ρ > 0.81; p < 0.001) where the strongest correlation existed between baseline and 7-day post-stroke infarct volumes (ρ = 0.92; p < 0.001). The strongest correlation among the follow-up imaging was found between infarct volumes 7-day post-stroke and ≥30-day time points (ρ = 0.93; p < 0.001). Linear regression showed a close-to unity slope between 7-day and final infarct volumes (slope = 1.043; p < 0.001). Infarct expansion was higher in the non-recanalized group than the recanalized group at the 7-day (p = 0.001) and ≥30-day (p = 0.038) time points. CONCLUSIONS: Final infarct volume can be approximated as early as 7 days post-stroke. Final infarct volume approximation is significantly associated with recanalization status.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".