Bibliographic record
Abstract
Imaging of central-nervous-system (CNS) abnormalities is important in patients with mitochondrial disorders (MCDs) since the CNS is the organ second most frequently affected in MCDs and some of them are potentially treatable. Clinically relevant imaging techniques for visualization of CNS abnormalities in MCDs are computed tomography, magnetic resonance imaging, and MR-spectroscopy. The CNS abnormalities in MCDs visualized by imaging techniques include stroke-like lesions with cytotoxic or vasogenic edema, laminar cortical necrosis, basal ganglia necrosis, focal or diffuse white matter lesions, focal or diffuse atrophy, intra-cerebral calcifications, cysts, lacunas, hypometabolisation, lactacidosis, hemorrhages, cerebral hypo- or hyperperfusion, intra-cerebral artery stenoses, or moyamoya syndrome. The CNS lesions may proceed with or without clinical manifestations, why neuroimaging should be routinely carried out in all MCDs to assess the degree of CNS involvement. Some of these lesions may remain unchanged for years, some may show contiguous spread and progression, but some may even disappear, spontaneously or in response to medication. Dynamics of Stroke-like lesions may be positively influenced by L-arginine, dichloracetate, steroids, edavarone, or antiepileptics. Symptomatic treatment of CNS abnormalities in MCD patients may positively influence their outcome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".