Systemic lupus and malignancies
Bibliographic record
Abstract
PURPOSE OF REVIEW: Individuals with systemic lupus erythematosus (SLE) have an increased susceptibility to certain types of cancer. Given concerns focused on this issue, we present a review of this important topic. RECENT FINDINGS: In non-Hodgkin lymphoma (NHL), a several-fold increased risk is seen in SLE versus the general population. It has long been suspected that immunosuppressive drugs play a role in this risk, but there may be other important driving factors as well. Lupus disease activity may itself heighten the risk of lymphoma in diseases like SLE. Lung cancer risk also is increased in SLE; smoking appears to drive this risk. Additionally, cervical dysplasia risk is increased in SLE, particularly with immunosuppressive drug exposure. An altered clearance of cancer-related viral agents in SLE (due to the disease and/or immunosuppression) may contribute to this risk and may also drive the risk for other cancers (such as vulvovaginal and hepatic carcinomas) in SLE. On the positive side, one new and significant finding is that SLE patients seem to have a decreased risk of certain nonhematologic cancers (breast, ovarian, endometrial, and prostate). SUMMARY: Though much has been learnt so far regarding the risk in SLE, much yet remains unknown.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".