Editorial: Prognostic and predictive factors in autoimmune connective tissue disorders
Bibliographic record
Abstract
Prognostic and predictive factors in autoimmune connective tissue disorders Autoimmune connective tissue diseases (CTD) are a complex group of diseases involving multiple organs and resulting in devastating visceral complications and even death.One of the most recognized challenges related to them is their variable disease courses that range from indolent ones to rapidly progressive clinical disease trajectories.This disease-associated heterogeneity is associated with differences in the severities of organ involvement, disease-specific damage accrual, autoantibody profiles, response(s) to treatment, and, ultimately, long-term survival.Hence, prognostic biomarkers that are ideally associated with the pathogenesis of these diseases are desperately needed.In this Research Topic, "Prognostic and Predictive Factors in Autoimmune Connective Tissue Disorders", submissions were solicited for original research articles, brief research reports, and review papers with a particular emphasis on novel serological biomarkers of disease severity, evaluation of gene expression and protein levels of inflammatory mediators, adhesion proteins, cytokines, and pro-resolving molecules, recent applications of routinary laboratory parameters and instrumental techniques for assessing disease severity and systemic involvement, and development of new multi-parametrical disease activity score for autoimmune CTD.Since the Research Topic was open to submission, 39 manuscripts were submitted, of which 27 were rejected, and 12 were published.The Research Topic was viewed about 20,500 times, highlighting the scientific community's interest in the subject, and its relevance to patients.An original research article by Atzeni et al. evaluated the soluble receptor for advanced glycation end products (RAGE) and its ligand high mobility group box 1 (HMGB1) as a potential predictor of pulmonary arterial hypertension (PAH) in systemic sclerosis (SSc).SSc (in general) and PAH (in particular) are characterized by a systemic progressive obliterative vasculopathy.Both interstitial lung disease (ILD) and PAH significantly impact the long-term survival of SSc patients.Early in the disease, SSc patients may develop pulmonary hypertension which may stem from ILD or PAH.Notably, there are no Frontiers in Immunology frontiersin.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".