Abstract TP147: Assessment of Inter-Rater Reliability of Fazekas Scoring on Magnetic Resonance Imaging in Patients With Sickle Cell Disease
Bibliographic record
Abstract
Background: White matter disease is a common phenomenon in patients with sickle cell disease, that has been linked to cognitive impairment. However, there is no standardized approach for quantification of the cerebral disease burden. The Fazekas score is widely used to quantify the burden of white matter disease in chronic small vessel disease. However, its utility in sickle cell disease has not yet been established. We aimed to assess its, interrater reliability in this patient population. Methods: A patient cohort was compiled for the purpose of a research ethics board (REB) approved retrospective study of consecutive adult patients with sickle cell disease, each of whom underwent MRI/MRA between the year 2017 and 2019. All MRI/MRA studies were performed on 3-Tesla MRI. Two neuroradiologists independently assessed the axial FLAIR MRI brain sequence, for all patients, with the sole focus of assigning a Fazekas score (0-3) to each study, as a means of quantifying the burden of ischemic white matter lesions. The neuroradiologists were blinded to the scoring assigned by their counterpart and to the clinical information. Cohen’s weighted Kappa was used as a measure of agreement between readers. Results: Ninety patients with a median age of 31 and 45/90 (50%) women were included. The expected agreement was 74.65%, with an observed agreement of 94.44% between readers, with a weighted Kappa of 0.7808. Conclusion: On the basis of this study, there is good inter-rater reliability of Fazekas scoring on axial FLAIR MRI brain sequence in patients with sickle cell disease, even though the underlying pathophysiology of white matter lesions in this patient population might vary compared to individuals with chronic small vessel disease. The Fazekas is a promising measure that could easily be integrated in systematic evaluation of cerebrovascular lesions of adults with sickle cell disease.
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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 | 0.027 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".