Should Magnetic Resonance Angiography Be Used for Screening of Intracranial Aneurysm in Adults with Sickle Cell Disease?
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) is used in patients with sickle cell disease (SCD) to detect silent cerebral infarcts. MR angiography (MRA) can identify arterial stenoses and intracranial aneurysms (ICANs) associated with SCD. In this study, we aimed to estimate the prevalence of ICANs in asymptomatic adult patients with SCD referred from the SCD clinic for routine screening by MRI/MRA using a 3T-MRI scanner. Findings were independently reviewed by two neuroradiologists. Between 2016 and 2020, 245 asymptomatic adults with SCD were stratified according to genotype (SS/S-β0thalassemia and SC/Sβ+). ICANs were found in 27 patients (11%; 0.95 CI: 8–16%). ICANs were more frequent in SS/S-β0thalassemia patients (20/118 or 17%; 0.95 CI: 11–25%) than in SC/βb+ patients (7/127 or 6%; 0.95 CI: 2–11%; p = 0.007). Individuals with SCD (particularly SS/S-β0thalassemia) have a higher prevalence of ICANs than the general population. We believe that MRA should be considered in the current American Society of Hematology guidelines, which already contain a recommendation for MRI at least once in adult SCD patients. However, the clinical significance of preventive treatment of unruptured aneurysms remains controversial.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".