Bibliographic record
Abstract
The purpose of this chapter is to present some general principles of the management of inherited metabolic diseases using specific examples to illustrate various points. It is not meant to be a detailed guide to the specific treatment of any particular disease. Instead, it is intended to provide a conceptual scaffold to aid in understanding the strategy behind the management of various inborn errors of metabolism, particularly strategies involving environmental manipulation. A logical approach to treatment would be to determine how various point defects in metabolism cause disease, and to reverse or neutralize them, either by dietary, pharmacologic, or some other form of metabolic manipulation. However, in many cases, our understanding of how a particular point defect in metabolism produces disease is still incomplete. Often the abnormality is metabolically or physically inaccessible to environmental manipulation. In the discussion to follow, examples are provided of how rational approaches to treatment grew out of an understanding of the primary and secondary consequences of inborn errors of metabolism. The emphasis is on instances in which treatment is at least partially successful. Control of accumulation of substrate When disease is caused by accumulation of the substrate of a reaction that is impaired as a result of deficiency of an enzyme or transport protein, a reasonable approach to treatment would be to try to control levels of the toxic metabolite, either by decreasing its accumulation or accelerating its removal by alternative reactions.
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.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.125 | 0.044 |
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".