Hemodynamic Failure Staging With Blood Oxygenation Level–Dependent Cerebrovascular Reactivity and Acetazolamide‐Challenged ( <sup>15</sup> O‐)H <sub>2</sub> O‐Positron Emission Tomography Across Individual Cerebrovascular Territories
Bibliographic record
Abstract
Background Staging of hemodynamic failure (HF) in symptomatic patients with cerebrovascular steno‐occlusive disease is required to assess the risk of ischemic stroke. Since the gold standard positron emission tomography‐based perfusion reserve is unsuitable as a routine clinical imaging tool, blood oxygenation level–dependent cerebrovascular reactivity (BOLD‐CVR) with CO 2 is a promising surrogate imaging approach. We investigated the accuracy of standardized BOLD‐CVR to classify the extent of HF. Methods and Results Patients with symptomatic unilateral cerebrovascular steno‐occlusive disease, who underwent both an acetazolamide challenge ( 15 O‐)H 2 O‐positron emission tomography and BOLD‐CVR examination, were included. HF staging of vascular territories was assessed using qualitative inspection of the positron emission tomography perfusion reserve images. The optimum BOLD‐CVR cutoff points between HF stages 0–1–2 were determined by comparing the quantitative BOLD‐CVR data to the qualitative ( 15 O‐)H 2 O‐positron emission tomography classification using the 3‐dimensional accuracy index to the randomly assigned training and test data sets with the following determination of a single cutoff for clinical application. In the 2‐case scenario, classifying data points as HF 0 or 1–2 and HF 0–1 or 2, BOLD‐CVR showed an accuracy of >0.7 for all vascular territories for HF 1 and HF 2 cutoff points. In particular, the middle cerebral artery territory had an accuracy of 0.79 for HF 1 and 0.83 for HF 2, whereas the anterior cerebral artery had an accuracy of 0.78 for HF 1 and 0.82 for HF 2. Conclusions Standardized and clinically accessible BOLD‐CVR examinations harbor sufficient data to provide specific cerebrovascular reactivity cutoff points for HF staging across individual vascular territories in symptomatic patients with unilateral cerebrovascular steno‐occlusive disease.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".