Bibliographic record
Abstract
With interest we read the review article by Senthilvelan et al. about the neuro-radiological findings in patients with a mitochondrial disorder (MID) [1]. It was concluded that cerebral imaging would render help in pointing towards a MID if a mutation escapes detection on exome sequencing. The study is appealing but has several limitations which raise the following comments and concerns. The nature of stroke-like lesions (SLLs) is not sufficiently characterised. SLLs are a hallmark of MELAS but occur in other MIDs, such as MERRF, Saguenay-Lac St.-Jean cytochrome oxidase deficiency, Kearns-Sayre syndrome (KSS), OPA1-related disease, POLG1-related MIDs, ND3-related MID, Leigh syndrome, maternally inherited diabetes and deafness, ND4-related MID, MT-TV-related MID, and ND5-related MID [2]. SLLs are dynamic lesions which usually originate from the cortex and spread to adjacent cortical or subcortical regions within days, weeks, or even months. They usually regress in size thereafter, to reach a final stage. In the expanding phase SLLs are characterised by hyperintensity on T2/FLAIR, DWI, PWI, and hypointensity on oxygen-extraction fraction (OEF) MRI. FDG-PET shows hypometabolism. SLLs do not comply with a vascular territory and usually show a lactate peak on MR-spectroscopy. SLLs may end up with or without a structural cerebral lesion. Structural lesions as an endpoint of a SLL include white matter lesions, cortical or subcortical cysts, or laminar cortical necrosis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".