Bibliographic record
Abstract
To the Editor: The recently published paper by van Roon, et al addresses an interesting question about the presence of “scleroderma” pattern in different connective tissue diseases and its association with abnormal pulmonary function tests1. The authors have detected it in 88% of cases with systemic sclerosis (SSc; 35/40), in 17% of patients with systemic lupus erythematosus (SLE; 5/30) and in 13% of patients with rheumatoid arthritis (RA; 2/15). Of note, apart from its association with the presence of Raynaud phenomenon (RP) in connective tissue diseases, “scleroderma-like” capillaroscopic pattern could also be associated with the presence of cutaneous digital vasculitis2,3. “Scleroderma/scleroderma-like” is characterized with giant capillaries (diameter > 50 μm), hemorrhages, derangement, avascular areas, and neoangiogenesis. The presence of giant capillaries represents a mandatory criterion in the initial stages of this capillaroscopic pattern that may present as an isolated finding. Scleroderma-type capillaroscopic pattern is observed in the vast majority … Address correspondence to Dr. S.N. Lambova, Medical University – Plovdiv, Department of Propaedeutics of Internal Diseases, 15A Vasil Aprilov Blvd., Plovdiv 4002, Bulgaria. E-mail: sevdalina_n{at}abv.bg
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".