Long term follow-up of the dietary intake in propionic acidemia
Bibliographic record
Abstract
Long-term dietary management of Propionic acidemia (PA) includes natural protein restriction, and supplementation with medical formula enriched with leucine (Leu) and free of valine (Val), isoleucine (Ileu), methionine (Met), and threonine (Thr). As PA medical formulas have high leucine content, concerns started to arise regarding potential long-term health risks of unbalanced leucine intake. PA patients have chronically low plasma levels of Ile and Val, which led to the paradoxical need to supplement with propiogenic single amino acids (AAs). Our report takes a retrospective look at the long-term dietary management of four patients and its reflection on their plasma amino acids. The patients' total protein intake was above the recommended dietary allowance (RDA) and had a high Leu/Val and Leu/Ile intake ratios in diet. Despite adequate total protein intake, patients had chronically low plasma Ile and Val and a high plasma Leu/Val and Leu/Ile ratios, which could be attributed to high Leu intake. We conclude that the best approach to PA dietary management is to only use medical formula with patients not meeting their RDA through natural protein, and to monitor plasma amino acids levels closely.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".