Systemic sclerosis: To subset or not to subset, that is the question
Bibliographic record
Abstract
Systemic sclerosis (SSc) is a heterogeneous disease with variability in autoantibody profiles, skin and internal organ involvement, disease trajectory, and survival. The ability to identify more homogeneous subsets of SSc patients has informed patient care and been an essential aspect of SSc research. In this article, the historic evolution of subsetting systems in SSc are described including clinically based SSc subsetting systems, their utility, strengths, and limitations. There is a shifting paradigm of SSc subsets, including biologic classification of SSc subsets and fully data-driven approaches to SSc subset classification, taking into consideration the needs of the SSc global community in the modern era and the ability to prognosticate patients with SSc. Cite this article as: Johnson SR, van den Hoogen F, Devakandan K, Matucci-Cerinic, Pope JE. Systemic sclerosis: To subset or not to subset, that is the question. Eur J Rheumatol 2020; 7(Suppl 3): S222-7.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".