The COVID-19 Global Rheumatology Alliance: evaluating the rapid design and implementation of an international registry against best practice
Bibliographic record
Abstract
OBJECTIVES: As the coronavirus disease 2019 pandemic developed there was a paucity of data relevant to people living with rheumatic disease. This led to the development of a global, online registry to meet these information needs. This manuscript provides a detailed description of the coronavirus disease 2019 Global Rheumatology Alliance registry development, governance structure, and data collection, and insights into new ways of rapidly establishing global research collaborations to meet urgent research needs. METHODS: We use previously published recommendations for best practices for registry implementation and describe the development of the Global Rheumatology Alliance registry in terms of these steps. We identify how and why these steps were adapted or modified. In Phase 1 of registry development, the purpose of the registry and key stakeholders were identified on online platforms, Twitter and Slack. Phase 2 consisted of protocol and data collection form development, team building and the implementation of governance and policies. RESULTS: All key steps of the registry development best practices framework were met, though with the need for adaptation in some areas. Outputs of the registry, two months after initial conception, are also described. CONCLUSION: The Global Rheumatology Alliance registry will provide highly useful, timely data to inform clinical care and identify further research priorities for people with rheumatic disease with coronavirus disease 2019. The formation of an international team, easily able to function in online environments and resulting in rapid deployment of a registry is a model that can be adapted for other disease states and future global collaborations.
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 | 0.755 | 0.761 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.008 | 0.027 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".