Infections in the lupus patient: perspectives on prevention
Bibliographic record
Abstract
PURPOSE OF REVIEW: Infections are one of the most common causes of morbidity, hospitalization and death in patients with systemic lupus erythematosus (SLE). The aim of the review is to describe an approach to screening and prevention of infections in patients with SLE based on recent evidence. RECENT FINDINGS: This review summarizes what is known about the incidence and risk factors for infection in SLE as well as the limitations of the current literature. An approach to screening for infections such as tuberculosis and viral hepatitis is described as well as use of prophylactic agents and vaccinations. SUMMARY: We recommend screening for infectious comorbidities such as tuberculosis and viral hepatitis at the first clinical encounter in patients with lupus in addition to recommending pneumococcal vaccination and yearly influenza vaccination. There is currently limited evidence to support antibiotic prophylaxis for SLE patients on immunosuppressive agents to prevent penumocystis or to support screening for cytomegalovirus and further study is required. Lastly, timely antibiotic treatment in patients with lupus who are hospitalized with infectious complications is important, as delayed antibiotic treatment may be associated with increased mortality.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".