Another Consequence of Severe Lupus: Invasive Fungal Disease
Bibliographic record
Abstract
Infections are a substantial cause of morbidity and mortality in patients with systemic lupus erythematosus (SLE), accounting for one-quarter of all deaths1,2. The mechanisms for increased susceptibility to infections in patients with SLE are classically attributed to disease- and treatment-related factors. Polymorphisms in the mannose-binding lectin3 protein, which helps in host defense by activating the complement cascade, have been reported as a risk factor primarily for bacterial infections in SLE3. Patients with SLE may also have acquired hypogammaglobulinemia (as reviewed by Yong, et al 4) or hyposplenism, which leads to increased susceptibility to infections with encapsulated organisms. Acquired neutropenia has also been cited as a risk factor for infections in SLE5 and for fungal infections in other populations6. Corticosteroids are frequently necessary in the treatment of SLE, resulting in diminished cellular immunity. Immunosuppressant therapy also clearly increases the risk of bacterial infection7. Invasive fungal infections were initially defined in 20028, with revisions occurring in 2008, including a change in terminology to “invasive fungal disease” (IFD)9 by the European Organization for Research and Treatment of Cancer/Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses Study Group (EORTC/MSG) Consensus Group. Proven IFD are subdivided into molds ( Aspergillus ) and yeasts ( Candida species and Cryptococcus ) and depend on culture or microscopic analysis from sterile anatomic sites, blood cultures, or cryptococcal antigen in cerebrospinal fluid. Separate criteria … Address correspondence to Dr. Barber; E-mail: cehbarbe{at}ucalgary.ca
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".