Identifying Significant Changes in Cerebrovascular Reactivity to Carbon Dioxide
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Changes in cerebrovascular reactivity can be used to assess disease progression and response to therapy but require discrimination of pathology from normal test-to-test variability. Such variability is due to variations in methodology, technology, and physiology with time. With uniform test conditions, our aim was to determine the test-to-test variability of cerebrovascular reactivity in healthy subjects and in patients with known cerebrovascular disease. MATERIALS AND METHODS: Cerebrovascular reactivity was the ratio of the blood oxygen level-dependent MR imaging response divided by the change in carbon dioxide stimulus. Two standardized cerebrovascular reactivity tests were conducted at 3T in 15 healthy men (36.7 ± 16.1 years of age) within a 4-month period and were coregistered into standard space to yield voxelwise mean cerebrovascular reactivity interval difference measures, composing a reference interval difference atlas. Cerebrovascular reactivity interval difference maps were prepared for 11 male patients. For each patient, the test-retest difference of each voxel was scored statistically as z-values of the corresponding voxel mean difference in the reference atlas and then color-coded and superimposed on the anatomic images to create cerebrovascular reactivity interval difference z-maps. RESULTS: There were no significant test-to-test differences in cerebrovascular reactivity in either gray or white matter (mean gray matter, P = .431; mean white matter, P = .857; paired t test) in the healthy cohort. The patient cerebrovascular reactivity interval difference z-maps indicated regions where cerebrovascular reactivity increased or decreased and the probability that the changes were significant. CONCLUSIONS: Accounting for normal test-to-test differences in cerebrovascular reactivity enables the assessment of significant changes in disease status (stability, progression, or regression) in patients with time.
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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 | 0.000 | 0.000 |
| 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".