Moving Beyond a Single Perfusion Threshold to Define Penumbra
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The mismatch lesion volumes defined by perfusion-weighted imaging exceeding diffusion-weighted imaging have been used as a marker of ischemic penumbral tissue. Defining the perfusion lesion by thresholding has shown promise as a practical tool; several positron emission tomography studies have indicated a more probabilistic relationship between perfusion and infarction. Here, we used a randomized controlled trial dataset of tissue-type plasminogen activator 3 to 6 hours after stroke to: (1) quantify the relationship between severity of hypoperfusion (measured by Tmax) and risk of infarction; (2) exploit this relationship to present a novel definition of mismatch based on infarct probabilities rather than dichotomies; and (3) examine the treatment response in the subgroup of patients with mismatch by the new definition. METHODS: Patients from the Echoplanar Imaging Thrombolytic Evaluation Trial (EPITHET) were included. Baseline perfusion-weighted imaging and 90-day T2-weighted imaging were coregistered. Perfusion-weighted imaging lesion volumes were divided into 10 Tmax delay strata, and infarct risk was defined as the fraction of the tissue at a given Tmax strata that progressed to infarction by day 90. RESULTS: Sixty-two patients were studied. Infarct risk was an increasing function of Tmax for all subgroups, including the whole cohort. The probabilistic approach outperformed all Tmax thresholds, with exception of the Tmax ≥ 10 threshold, for which it was only favored by a trend. CONCLUSIONS: Infarct risk and treatment effect increased with severity of perfusion abnormalities. This suggests that a severity-weighted mismatch definition may define penumbral tissue more accurately.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".