The severity of anaemia depletes cerebrovascular dilatory reserve in children with sickle cell disease: a quantitative magnetic resonance imaging study
Bibliographic record
Abstract
Summary Overt ischaemic stroke is one of the most devastating complications in children with sickle cell disease (SCD). The compensatory response to anaemia in SCD includes an increase in cerebral blood flow (CBF) by accessing cerebrovascular dilatory reserve. Exhaustion of dilatory reserve secondary to anaemic stress may lead to cerebral ischaemia. The purpose of this study was to investigate CBF and cerebrovascular reactivity (CVR) using magnetic resonance imaging (MRI) in children with SCD and to correlate these with haematological markers of anaemia. Baseline CBF was measured using arterial spin labelling. Blood‐oxygen level‐dependent MRI in response to a CO2 stimulus was used to acquire CVR. In total, 28 children with SCD (23 not on any disease‐modifying treatment, 5 on chronic transfusion) and 22 healthy controls were imaged using MRI. Transfusion patients were imaged at two time points to assess the effect of changes in haematocrit after a transfusion cycle. In children with SCD, CBF was significantly elevated compared to healthy controls, while CVR was significantly reduced. Both measures were significantly correlated with haematocrit. For transfusion patients, CBF decreased and CVR increased following a transfusion cycle. Lastly, a significant correlation was observed between CBF and CVR in both children with SCD and healthy controls.
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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".