Near-Infrared Spectroscopy Demonstrates the Benefit of Erythracytapheresis in Sickle Cell Disease Adult Patients with Cerebral Vasculopathy
Bibliographic record
Abstract
BACKGROUND: Cerebral vasculopathy can induce chronic cerebral hypoperfusion leading to stroke in patients with sickle cell disease (SCD) and is treated by blood exchange transfusion (BET). However, no prospective clinical study has demonstrated the benefit of BET in adults with SCD and cerebral vasculopathy. Near Infrared Spectroscopy (NIRS) is a recent non-invasive method complementary to Magnetic Resonance Imaging (MRI). We evaluated cerebral perfusion using NIRS during erythracytapheresis in patients with SCD with and without steno-occlusive arterial disease. METHODS: We conducted a monocentric, prospective study in 16 adults with SCD undergoing erythracytapheresis in 2014. Among them, 10 had cerebral steno-occlusive arterial disease. NIRS measured the relative amounts of oxyhemoglobin (OxyHb), deoxyhemoglobin (DeoxyHb) and total hemoglobin (Total Hb) in brain tissue and in muscle. RESULTS: In cerebral hemispheres associated with steno-occlusive arterial disease, we observed a significant increase of OxyHb and Total Hb during BET, without modification of DeoxyHb. CONCLUSION: Using NIRS during BET showed that BET improves cerebral perfusion in adult patients with SCD with cerebral vasculopathy.
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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 | 0.000 | 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) | 0.001 | 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".