P.054 Bone marrow transplant restores Cerebrovascular Reactivity (CVR) in Sickle Cell Disease (SCD): a case presentation
Bibliographic record
Abstract
Background: A diagnosis of SCD in childhood confers a 200-fold increase in the risk of arterial ischemic stroke. Blood flow velocity measures provide better identification of ischemic risk compared to angiography. This indicates that steno-occlusive arteriopathy is not the singular causative factor. Cerebrovascular reactivity allows for augmentation of cerebral blood flow when needed. Kosinski et al in 2016 demonstrated a direct correlation between CVR and hematocrit levels in SCD. We report a case where CVR persistently normalized in an SCD patient following bone marrow transplant therapy (BMT). Methods: A nine-month-old SCD patient presented with right AIS. Angiography revealed a bilateral Moya-Moya like arteriopathy. A TCD study was normal while a CVR-MRI study revealed markedly impaired reactivity in the entire anterior circulation. Haemaglobin-S at that time was 20.2 %. BMT was performed at age four due to frequent sickle cell crises. Results: One year post-transplant, CVR had dramatically improved in areas previously shown to have impairment (haemoglobin-S 0%). Neuroimaging five years post-transplant showed no further arteriopathy and persistently normalized CVR. Conclusions: BMT therapy resulted in the arrest of progressive intracranial arteriopathy and persistently restored vascular reserve. SCD might not only produce global hematological effects but also triggers local processes such as endothelial dysfunction and vascular inflammation that impair cerebrovascular function.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".