P.078 Clinical trials in children with Down Syndrome: now and future
Bibliographic record
Abstract
Background: Down syndrome (DS) is the most common genetic cause of intellectual disability.Although progress in managing co-morbidities has improved life expectancy, no therapeutic options have showed to significantly improve intellectual deficiencies.The current focus of the pharmacological treatment of DS is on the improvement of the cognitive impairment that is probably due to neurodevelopmental alterations,neurotransmitter alterations and neurodegeneration,and is also targeted to the overexpression of selected genes on HSA21. Methods: We review the clinical trials of the last 5 years focusing on the cognitive improvement of children with DS. Results: We report the results of therapeutic trials concerning selective negative allosteric regulators of the GABAAa5 receptor, NMDA antagonists, Kinase inhibitors of DYRK1A, folinic acid and thyroid hormone supplementation, activators of serotonergic and cholinergic system. Conclusions: The incomplete understanding of individual phenotypic variability, natural history, lack of biomarkers, no adapted neuropsychological tests, placebo effect, epigenetic effect have limited our capacity to succeed, even when promising drugs are tested.We need new tools and models will allow a better understanding of the pathophysiology. We also need to create more sensitive and realistic outcome mesures to quantify disease and therapeutic efficacy. The association of different therapeutic agents (epidrugs included) with cognitive stimulation could be a future strategy.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".