Developing a provisional, international Minimal Dataset for Juvenile Dermatomyositis: for use in clinical practice to inform research
Bibliographic record
Abstract
BACKGROUND: Juvenile dermatomyositis (JDM) is a rare but severe autoimmune inflammatory myositis of childhood. International collaboration is essential in order to undertake clinical trials, understand the disease and improve long-term outcome. The aim of this study was to propose from existing collaborative initiatives a preliminary minimal dataset for JDM. This will form the basis of the future development of an international consensus-approved minimum core dataset to be used both in clinical care and inform research, allowing integration of data between centres. METHODS: A working group of internationally-representative JDM experts was formed to develop a provisional minimal dataset. Clinical and laboratory variables contained within current national and international collaborative databases of patients with idiopathic inflammatory myopathies were scrutinised. Judgements were informed by published literature and a more detailed analysis of the Juvenile Dermatomyositis Cohort Biomarker Study and Repository, UK and Ireland. RESULTS: A provisional minimal JDM dataset has been produced, with an associated glossary of definitions. The provisional minimal dataset will request information at time of patient diagnosis and during on-going prospective follow up. At time of patient diagnosis, information will be requested on patient demographics, diagnostic criteria and treatments given prior to diagnosis. During on-going prospective follow-up, variables will include the presence of active muscle or skin disease, major organ involvement or constitutional symptoms, investigations, treatment, physician global assessments and patient reported outcome measures. CONCLUSIONS: An internationally agreed minimal dataset has the potential to significantly enhance collaboration, allow effective communication between groups, provide a minimal standard of care and enable analysis of the largest possible number of JDM patients to provide a greater understanding of this disease. This preliminary dataset can now be developed into a consensus-approved minimum core dataset and tested in a wider setting with the aim of achieving international agreement.
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 | 0.028 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".