What investigations are needed to optimally monitor for malignancies in SLE?
Bibliographic record
Abstract
OBJECTIVE: The overall cancer incidence risk in systemic lupus erythematosus (SLE) is approximately 15%-20% more than in the general population. Nevertheless, to date, the optimal malignancy screening measures in SLE remain undefined. Our objective is to determine what investigations are needed to optimally monitor for malignancies in SLE in order to inform upcoming Canadian Rheumatology Association recommendations. METHODS: We conducted a systematic search looking at three scientific sources, Embase, Medline and Cochrane, in an attempt to identify cancer screening recommendations for patients with SLE. We used a filter for observational studies and included articles published in 2000 and onward. RESULTS: The initial search strategy led to 986 records. After removal of duplicates and articles unrelated to SLE, we were left with 497 titles. From those, 79 research articles on cancer incidence in SLE were isolated and reviewed. Of the 79 original research papers, 25 offered screening recommendations, 14 suggested additional cancer screening whereas 11 studies simply promoted adherence to general population screening measures. The suggestions for more rigorous screening included recommending human papilloma virus testing in addition to routine cervical screening, and/or that cervical screening should be performed annually and/or suggested urine cancer screening in SLE patients with a history of cyclophosphamide exposure. CONCLUSIONS: We found no original research studies directly comparing cancer screening strategies in SLE. Generally, authors recommend adherence to general population screening measures, particularly cervical screening. This, possibly with adding targeted screening in special cases (e.g. annual urine cytology in patients with prior cyclophosphamide exposure, and considering existing lung cancer screening guidelines for past heavy smokers), may be a reasonable approach for cancer screening in SLE.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".