MRI Interpretation Errors in Adult Patients with Medically Refractory Epilepsy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The aim is to determine the most common MRI interpretation errors in patients with medically refractory epilepsy (MRE) according to our experience in a tertiary academic center with a large volume epilepsy program, to raise awareness of the "blind spots" in imaging patients with MRE, and to highlight the importance of combining clinical and electroencephalographic information to obtain the most accurate diagnosis. MATERIALS AND METHODS: This is a retrospective observational study. All patients with MRE who underwent MRI brain (on 3T, with dedicated epilepsy protocol), and who were discussed at the weekly interdisciplinary epilepsy conference in our center between January 2008 and July 2023 were included. The initial MRI interpretation and final MRI interpretation results were reviewed. RESULTS: A total of 886 patients with MRE were included. Three hundred patients were MRI-negative (33.86%), diagnoses were missed in 95 patients (10.7%), a second diagnosis was missed in patients with dual pathologies in 42 patients (4.74%), findings were misinterpreted in 4 patients (0.45%), an overcall was made in 2 patients (0.2%), and in 5 patients the correct diagnosis was discussed but erroneously deemed absent on initial interpretation (0.56%), resulting in a total of 148 discrepant MRI reports. Mesial temporal sclerosis comprised the most common pathology encountered overall, followed by encephalomalacia related to prior insult, enlarged amygdala (EA), malformations of cortical development, cavernoma, and ulegyria. The relative proportion of missed focal cortical dysplasia, encephalocele, and EA accounted for the most common misses/misdiagnoses. CONCLUSIONS: Evaluation of patients with MRE requires excellent interdisciplinary care. Input from all members of the interdisciplinary team is essential for accurate interpretation of MRI in patients with MRE for the neuroradiologist. We hope to inform radiologists of commonly overlooked pathologies in MRI brain interpretation for patients with epilepsy. In doing so, we want to maximize the yield of initial MRI interpretation in these patients.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".