Bibliographic record
Abstract
Mitochondrial encephalomyopathies have shifted from being regarded just a few years ago as a category of rare metabolic diseases to a current recognition that they are much more common than previously thought, are a large and heterogeneous group of distinct diseases, and that they may occur not only as primary biochemical and genetic defects, but also as secondary defects in a wide spectrum of both genetic and acquired diseases of the nervous system ranging from Pompe disease (glycogenosis II, acid maltase deficiency), spinal muscular atrophy, septo-optic-pituitary dysplasia, some inflammatory myopathies, and secondary to a broad range of drugs that include immunosuppressive and chemotherapeutic agents, statins and valproic acid. Most of the research on mitochondrial diseases has been generated, not surprisingly, in the countries with the greatest funding and biochemical laboratory resources for investigating them, including detailed biochemical and genetic studies, neuroimaging and neuropathological studies, mainly from western Europe and North America. It is therefore encouraging to see studies of mitochondrial diseases eminating also from countries with fewer resources, such as the paper by Selim et al. [1] from Egypt, in this current issue of the JPN.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".