Innovating Therapies for Down Syndrome: An International Virtual Conference of the T21 Research Society
Bibliographic record
Abstract
Research focused on Down syndrome continued to gain momentum in the last several years and is advancing our understanding of how trisomy 21 (T21) modifies molecular and cellular processes. The Trisomy 21 Research Society (T21RS) is the premier scientific organization for researchers and clinicians studying Down syndrome. During the COVID pandemic, T21RS held its first virtual conference program, sponsored by the University of California at Irvine, on June 8-10, 2021 and brought together 342 scientists, families, and industry representatives from over 25 countries to share the latest discoveries on underlying cellular and molecular mechanisms of T21, cognitive and behavioral changes, and comorbidities associated with Down syndrome, including Alzheimer's disease and Regression Disorder. Presentations of 91 cutting-edge abstracts reflecting neuroscience, neurology, model systems, psychology, biomarkers, and molecular and pharmacological therapeutic approaches demonstrate the compelling interest and continuing advancement toward innovating biomarkers and therapies aimed at ameliorating health conditions associated with T21.
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 | 0.022 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".