Assessing Cerebrovascular Resistance in Patients With Sickle Cell Disease
Bibliographic record
Abstract
In patients with sickle cell disease (SCD) the delivery of oxygen to the brain is compromised by anemia, abnormal rheology, and steno-occlusive vascular disease. Meeting demands for oxygen delivery requires compensatory features of brain perfusion. The cerebral vasculature’s regulatory function and reserves can be assessed by observing the flow response to a vasoactive stimulus. In a traditional approach we measured voxel-wise change in Blood Oxygen-Level Dependent (BOLD) MRI signal as a surrogate of cerebral blood flow (CBF) in response to a linear progressive ramping of end-tidal partial pressure of carbon dioxide (PETCO2). Cerebrovascular reactivity (CVR) was defined as ΔBOLD/ΔPETCO2. We used a computer model to fit a virtual sigmoid resistance curve to the progressive CBF response to the stimulus, enabling the calculation of resistance parameters: amplitude, midpoint, range response, resistance sensitivity and vasodilatory reserve. The quality of the resistance sigmoid fit was expressed as the r2 of the fit. We tested 35 patients with SCD, as well as 24 healthy subjects to provide an indication of the normal ranges of the resistance parameters. We found that gray matter CVR and resistance amplitude, range, reserve, and sensitivity are reduced in patients with SCD compared to healthy controls, while resistance midpoint was increased. This study is the first to document resistance measures in adult patients with SCD. It is also the first to score these vascular resistance measures in comparison to the normal range. We anticipate these data will complement the current understanding of the cerebral vascular pathophysiology of SCD, identify paths for therapeutic interventions, and provide biomarkers for monitoring the progress of the 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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".