Inborn Errors of Ketone Body Metabolism and Transport
Bibliographic record
Abstract
Major progress occurred in understanding inborn errors of ketone body transport and metabolism between the International Congresses on Inborn Errors of Metabolism in Barcelona (2013) and Rio de Janeiro (2017). These conditions impair either ketogenesis (presenting as episodes of hypoketotic hypoglycemia) or ketolysis (presenting as ketoacidotic episodes); for both groups, immediate intravenous glucose administration is the most critical and (mHGGCS, HMGCS2) effective treatment measure. Ketogenesis Deficiencies:New biomarkers were described for mitochondrial 3-hydroxy-3-methylglutaryl-CoA synthase (mHGGCS, HMGCS2) deficiency. New patient series refined clinical knowledge of 3-hydroxy-3-methylglutaryl-CoA lyase (HGGCL, HMGCL) deficiency. Although affected humans have not been described, two animal model phenotypes are pertinent: zebrafish deficient in monocarboxylate transporter 7 (MCT7, slc16a6) (decreased ketone body exit from hepatocytes) or mice lacking D-3-hydroxy-n-butyrate dehydrogenase (BDH1, BDH1) (isolated hyperacetoacetatemia; fatty liver). Ketolysis Deficiencies:Monocarboxylate transporter 1 (MCT1, SLC16A1) deficiency is a newly described defect of ketone body transport, joining deficiencies of succinyl-CoA:3-oxoacid CoA transferase (SCOT, OXCT1) and methylacetoacetyl-CoA thiolase (MAT, ACAT1). Some heterozygotes for MCT1 or SCOT deficiency develop ketoacidosis.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".