Report on the rare disease consortium Japan inaugural symposium - July 18, 2023, shonan health innovation park, Japan
Bibliographic record
Abstract
The Rare Disease Consortium Japan (RDCJ) is a newly formalized cross-sector initiative launched to address the urgent and growing needs of individuals living with rare diseases in Japan, aiming to support a wide range of rare conditions, including-but not limited to-neuromuscular diseases. RDCJ hosted its inaugural symposium on July 18, 2023, at the Shonan Health Innovation Park. This symposium aimed to address the unique challenges of rare diseases through a collaborative approach involving industry, government, academia, patients, and the community. The event brought together these stakeholders to discuss the current state and future directions of rare disease research and treatment in Japan. The event featured a range of speakers and panel discussions on the state of drug development, patient journeys, and the future of patient-centered healthcare services. Highlights included a keynote on antisense oligonucleotide therapeutics and sessions on patient-centered healthcare, human-centered design in healthcare, and innovative approaches to rare disease treatment. This report provides a detailed overview of the symposium, the key takeaways from each session, and the future directions for RDCJ.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.081 | 0.024 |
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".