Registries in systemic sclerosis: a worldwide experience
Bibliographic record
Abstract
SSc is a multisystem disease characterized by an unpredictable course, high mortality and resistance to therapy. The complexity and severity of SSc is a growing burden on the health-care systems. As a result, researchers are seeking new therapeutic strategies for effectively managing these patients. Disease registries are used to support care management efforts for groups of patients with chronic diseases and are meaningful to capture and track key patient information to assist the physicians in managing patients. For these reasons, SSc surveys, research associations and consortiums are pivotal to conduct ongoing research and data collection to enhance disease knowledge and support research projects. Currently, there are several national SSc registries in the UK, Germany, USA, Canada, Brazil and Australia. There is also an international registry established by the European League Against Rheumatism scleroderma trial and research (EUSTAR) called minimal essential data set (MEDS) Online, which collects data from over 8000 patients from 92 centres worldwide, including 21 European centres and 9 centres outside Europe. By collecting, analysing and disseminating data on disease progression and patient responses to long-term disease management strategies, registries help to improve understanding of the disease and keep medical professionals up to date on the latest advances.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".