Cancer risk in childhood-onset systemic lupus
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to assess cancer incidence in childhood-onset systemic lupus erythematosus (SLE). METHODS: We ascertained cancers within SLE registries at 10 pediatric centers. Subjects were linked to cancer registries for the observational interval, spanning 1974 to 2009. The ratio of observed to expected cancers represents the standardized incidence ratio (SIR) or relative cancer risk in childhood-onset SLE, versus the general population. RESULTS: There were 1020 patients aged <18 at cohort entry. Most (82%) were female and Caucasian; mean age at cohort entry was 12.6 years (standard deviation (SD) = 3.6). Subjects were observed for a total of 7,986 (average 7.8) patient-years. Within this interval, only three invasive cancers were expected. However, 14 invasive cancers occurred with an SIR of 4.7, 95% confidence interval (CI) 2.6 to 7.8. Three hematologic cancers were found (two non-Hodgkin’s lymphoma, one leukemia), for an SIR of 5.2 (95% CI 1.1 to 15.2). The SIRs stratified by age group and sex, were similar across these strata. There was a trend for highest cancer occurrence 10 to 19 years after SLE diagnosis. CONCLUSIONS: These results suggest an increased cancer risk in pediatric onset SLE versus the general population. In absolute terms, this represents relatively few events. Of note, risk may be highest only after patients have transferred to adult care.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".