Prostate cancer in systemic lupus erythematosus
Bibliographic record
Abstract
Our research objective was to estimate prostate cancer risk in systemic lupus erythematosus (SLE), relative to the age-matched general population. A progressive literature review was performed to identify SLE cohort studies with cancer registry linkage for cancer ascertainment. Data were pooled from four studies of large SLE cohorts who met these criteria. The total number of prostate cancers observed was derived by pooling the incident cases across all studies. The total expected number of prostate, derived from applying appropriate general population cancer incidence data to the observed number of patient-years of follow-up for each study, was similarly determined. The parameter of interest was the standardized incidence ratio (SIR), the ratio of observed to expected malignancies. The four studies together provided a pool of 6,068 male SLE patients observed for a total of 38,186 patient-years (mean 6.3 years). Within these subjects, 80 prostate cancers were observed. In each contributing study, the number of cancers expected far exceeded that observed. The pooled SIR estimate for prostate cancer risk in males with SLE, compared to the general population, was 0.72 (95% CI 0.57, 0.89). These data suggest a decreased risk of prostate cancer in SLE; more definite conclusions require additional data. As alterations in androgen pathways can potentially alter prostate risk, a lower risk of prostate cancer in SLE could possibly be due to low hypoadrenergic states which some believe may occur in men with SLE; underlying genetic factors could also be at play. Further study of these issues in large cohorts is needed.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".