Bibliographic record
Abstract
The discovery of the antineutrophil cytoplasmic antibody (ANCA) has brought us closer to understanding the mechanisms involved in the development of certain vasculitides. In spite of this, an ideal classification scheme for vasculitis has eluded the literature. This is especially true for those vasculitides that we believe are related to ANCA, a group of disorders sometimes referred to as ANCA-associated vasculitides, or AAV [Wegener’s granulomatosis (WG), Churg-Strauss syndrome (CSS), microscopic polyangiitis (MPA), and the occasionally referred to renal limited vasculitis]. In this issue of The Journal , Linder, et al from the University of Heidelberg once again tackle the issue of classification, specifically WG versus MPA, using an artificial neural network (ANN)1. There are many issues when assessing classification criteria. Historically, vasculitis was initially described histologically in a patient with necrotizing arteritis in 1866 by Kussmaul and Meir, who named the disease “periarteritis nodosa” (PAN). By the 1950s, many investigators realized that there were clinically and pathologically distinct forms of arteritis and that many if not all the vessels involved smaller arteries. In 1952, Zeek proposed the generic term “necrotizing vasculitis” to designate 5 distinct types of systemic vasculitis defined by clinical and pathological findings2. All subsequent classifications are to some extent derived from Zeek’s classification. Also by the 1950s two variant forms of vasculitis with … Address correspondence to Dr. Khalidi; E-mail: naderkhalidi{at}sympatico.ca
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 | 0.045 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.010 | 0.034 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".