Steal physiology is spatially associated with cortical thinning
Bibliographic record
Abstract
BACKGROUND: The physiological impact of severely impaired cerebral autoregulatory vascular reactivity on cortical integrity is unknown. The purpose of this study is to determine the relationship between severe impairment of autoregulatory flow control associated with steal phenomenon and its impact on cortical thickness. METHODS: 250 blood oxygen level dependent (BOLD) MRI cerebrovascular reactivity (CVR) studies were reviewed in order to identify subjects with severe unilateral exhausted cerebrovascular reserve demonstrating steal physiology but with normal appearing cortex on fluid attenuated inversion recovery imaging. 17 patients meeting the inclusion criteria were identified. A reconstructed inflated cortical surface map was created for every subject using Freesurfer software (http://surfer.nmr.mgh.harvard.edu/). The region of interest (ROI) reflecting the steal physiology was determined by overlaying the subject's CVR map on to the cortical surface map. This ROI was compared with the corresponding area in the healthy hemisphere which provided control cortical thickness measurement in each subject. RESULTS: The hemisphere with steal physiology showed an 8% thinner cortex (2.23+/-0.28 mm) than the corresponding healthy hemisphere (2.42+/-0.23 mm) (p=0.0005). CONCLUSIONS: Our findings indicate that a spatial correspondence exists between impairment of autoregulatory capacity with steal physiology and cortical thinning.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".