Cerebral Infarct Growth: Pathophysiology, Pragmatic Assessment, and Clinical Implications
Bibliographic record
Abstract
Cerebral ischemic injury occurs when blood flow drops below a critical level, resulting in an energy failure. The progressive transformation of hypoperfused viable tissue, the ischemic penumbra, into infarction is a mechanism shared by patients with ischemic stroke if timely reperfusion is not achieved. Yet, the pace at which this transformation occurs, known as the infarct growth rate (IGR), exhibits remarkable heterogeneity among patients, brain regions, and over time, reflecting differences in compensatory collateral flow and ischemic tolerance. We review (1) the pathophysiology of infarct growth, (2) the advantages and pitfalls of different approaches of IGR measurement, (3) research gaps for future studies, and (4) the clinical implications of stroke progressor phenotypes. The estimated average IGR in patients with acute large vessel occlusion stroke is 5.4 mL/h although there is wide variability based on ischemic stroke subtype, occlusion location, presence of collaterals, and patient baseline status. The IGR can be calculated using various pragmatic strategies, mostly either quantifying the extension of the infarct at a particular time and dividing this measure by the time that elapsed from symptom onset to imaging assessment or by using collateral blood flow status as a radiological surrogate marker. The IGR defines a spectrum of clinical stroke phenotypes, often dichotomized into fast and slow progressors. An IGR ≥10 mL/h and the perfusion metric hypoperfusion intensity ratio ≥0.5 are commonly used definitions of fast progressors. A nuanced understanding of the IGR and stroke progressor phenotypes could have clinical implications, including informing prognostication, acute decision-making in peripheral-to-comprehensive transfer patients eligible for thrombectomy, and selection for adjuvant neuroprotective agents.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".