Bibliographic record
Abstract
Vasculitides are disorders characterized by inflammation of the vessel walls, often caused by autoimmunity, but sometimes as a result of microbial invasion. Almost all types of microbes including bacteria, viruses, protozoa and fungi have been incriminated in the pathogenesis of vasculitis. Accurate etiological diagnosis is important since immunosuppressive treatment may lead to further deterioration if infection is the cause of vasculitis. Clinical features sometimes provide clues to the etiology. Further evaluation requires a focused and cost-effective plan of laboratory investigation. The investigations aim at establishing the diagnosis of vasculitis and identify the causative organism. An accurate diagnosis of vasculitis optimally requires histological examination and imaging. For infection-associated vasculitis, the identification of the organism requires studies of stained specimens, cultures, and/or detection of antigens and antibodies. Ideally, the treatment involves administration of an appropriate antimicrobial. In non-self-limiting types of vasculitides, glucocorticoids are needed when the symptoms are progressive, with vital organs involvement, and sometimes, when there is no antimicrobial agent of proven efficacy against the incriminated agent. Additional immunosuppressive agents or interventions must be considered when the disease is severe and/or post-infective immune mechanisms are involved in the pathogenesis, e.g., severe HBV- or HCV-associated vasculitides. Available preventative vaccinations are also crucial. The incidence of HBV-associated vasculitides dramatically decreased following HBV vaccination campaigns, and other infection-associated vasculitides may as well in the future.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".