Collaborative research in myositis-related disorders: MIHRA, a global shared community model
Bibliographic record
Abstract
Myositis International Health and Research Collaborative Alliance (MIHRA) is a newly formed purpose-built non-profit charitable research organization dedicated to accelerating international clinical trial readiness, global professional and lay education, career development and rare disease advocacy in IIM-related disorders. In its long form, the name expresses the community's scope of engagement and intent. In its abbreviation, MIHRA, conveys linguistic roots across many languages, that reflects the IIM community's spirit with meanings such as kindness, community, goodness, and peace. MIHRA unites the global multi-disciplinary community of adult and pediatric healthcare professionals, researchers, patient advisors and networks focused on conducting research in and providing care for pediatric and adult IIM-related disorders to ultimately find a cure. MIHRA serves as a resourced platform for collaborative efforts in investigator-initiated projects, consensus guidelines for IIM assessment and treatment, and IIM-specific career development through connecting research networks.MIHRA's infrastructure, mission, programming and operations are designed to address challenges unique to rare disease communities and aspires to contribute toward transformative models of rare disease research such as global expansion and inclusivity, utilization of community resources, streamlining ethics and data-sharing policies to facilitate collaborative research. Herein, summarises MIHRA operational cores, missions, vision, programming and provision of community resources to sustain, accelerate and grow global collaborative research in myositis-related disorders.
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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 | 0.134 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.011 | 0.045 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.006 | 0.042 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".