Bibliographic record
Abstract
Abstract: Wilson disease (WD) is rare genetic disorder that presents with varied phenotype that can at times make the diagnosis challenging. Medical treatments are available, but there are still unmet needs for patients. Since life-long therapy is necessary, adherence to medical therapy and best practices for monitoring and individualizing therapy continue to evolve. Studies are ongoing that address some of these issues. In the current review we focused our attention to recent advances in the diagnosis of WD, current medical treatments, future potential therapies and treatment monitoring. We include discussion of new methodology for detection and quantitation of ophthalmologic signs of WD, new brain imaging modalities for early detection of neurologic involvement in patients and potential new diagnostic methodology using blood samples that may be applicable to newborn screening and adult disease diagnosis. In addition, there are new strategies aimed at improving adherence and outcomes with currently available therapies, including once daily chelation dosing and discussion of the efficacy of different zinc salt compounds. With respect to new therapies with different mechanisms of action, we discuss studies on Bis-choline tetrathiomolybdate (TTM) in patients, pre-clinical studies of a novel chelator methanobactin and other animal studies exploring cures for WD with gene therapy using adeno-associated vectors (AAVs) that introduce ATP7B into liver cells. There are also promising advances in the more accurate measurement of non-ceruloplasmin bound copper and exchangeable copper in the circulation which would potentially help with monitoring and individualization of treatment and possibly play a role in future disease diagnosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".