Imaging of malformations of cortical development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Malformations of cortical development (MCD) include a broad range of disorders that result from disruption of the major steps of cortical development: cell proliferation in germinal zones, neuronal migration and cortical organization. With the improvement and increased utilization of modern imaging techniques, MCD have been increasingly recognized as a major cause of seizure disorders. The advent of Magnetic Resonance Imaging (MRI), in particular, has revolutionized the investigation and the treatment of patients with epilepsy. High-resolution MRI may elucidate the type, the extension and the localization of MCD; therefore, in a group of patients suffering from drug-resistant partial epilepsy (DRPE), MRI greatly contributes to the identification of subjects who are suitable for surgical treatment. In the recent past, many efforts were addressed to establish the MRI diagnostic criteria for a peculiar group of MCD, namely focal cortical dysplasias (FCD), histopathologically distinguished as types I and II. Some subtle FCD, which were previously cryptic to imaging investigation, can now be recognized by MRI, however their detection and specification remains challenging. This review will re-visit the neuroimaging findings, including structural MRI, PET, co-registered PET/MRI, MEG and diffusion tensor imaging (DTI) of FCD types I and II. Three major issues will be discussed: 1) the morphological MRI features of the FCDs, 2) the utility of PET and MEG and the use of co-registration methods and 3) diffusion tensor imaging (DTI) as a future modality of investigation, which may add additional informations regarding the microstructure of the grey matter (GM) and white matter (WM) in cortical dysplasia.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it