Assessment of cerebral blood flow with magnetic resonance imaging in children with sickle cell disease: A quantitative comparison with transcranial Doppler ultrasonography
Bibliographic record
Abstract
Abstract Introduction Transcranial Doppler ultrasonography ( TCD ) is a clinical tool for stratifying ischemic stroke risk by identifying abnormal elevations in blood flow velocity ( BFV ) in the middle cerebral artery ( MCA ). However, TCD is not effective at screening for subtle neurologic injury such as silent cerebral infarcts. To better understand this disparity, we compared TCD measures of BFV with tissue‐level cerebral blood flow ( CBF ) using arterial spin‐labeling MRI in children with and without sickle cell disease, and correlated these measurements against clinical hematologic measures of disease severity. Methods TCD and MRI assessment were performed in 13 pediatric sickle cell disease patients and eight age‐matched controls. Using MRI measures of MCA diameter and territory weight, TCD measures of BFV in the MCA [cm/s] were converted into units of CBF [ml min −1 100 g −1 ] for comparison. Results There was no significant association between TCD measures of BFV in the MCA and corresponding MRI measures of CBF in patients ( r = .28, p = .39) or controls ( r = .10, p = .81). After conversion from BFV into units of CBF , a strong association was observed between TCD and MRI measures ( r = .67, p = .017 in patients, r = .86, p = .006 in controls). While BFV in the MCA showed a lack of correlation with arterial oxygen content, an inverse association was observed for CBF measurements. Conclusions This study demonstrates that BFV in the MCA cannot be used as a surrogate marker for tissue‐level CBF in children with sickle cell disease. Therefore, TCD alone may not be sufficient for understanding and predicting subtle pathophysiology in this population, highlighting the potential clinical value of tissue‐level CBF .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".