Malignancy in Pediatric-onset Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To determine cancer incidence in a large pediatric-onset systemic lupus erythematosus (SLE) population. METHODS: Data were examined from 12 pediatric SLE registries in North America. Patients were linked to their regional cancer registries to detect cancers observed after cohort entry, defined as date first seen in the clinic. The expected number of malignancies was obtained by multiplying the person-years in the cohort (defined from cohort entry to end of followup) by the geographically matched age-, sex-, and calendar year-specific cancer rates. The standardized incidence ratio (SIR; ratio of cancers observed to expected) was generated, with 95% CI. RESULTS: A total of 1168 patients were identified from the registries. The mean age at cohort entry was 13 years (SD 3.3), and 83.7% of the subjects were female. The mean duration of followup was 7.6 years, resulting in a total observation period of 8839 years spanning the calendar period 1974-2009. During followup, fourteen invasive cancers occurred (1.6 cancers per 1000 person-yrs, SIR 4.13, 95% CI 2.26-6.93). Three of these were hematologic (all lymphomas), resulting in an SIR for hematologic cancers of 4.68 (95% CI 0.96-13.67). SIR were increased for both male and female patients, and across age groups. CONCLUSION: Although cancer remains a relatively rare outcome in pediatric-onset SLE, our data do suggest an increase in cancer for patients followed an average of 7.6 years. About one-fifth of the cancers were hematologic. Longer followup, and study of drug effects and disease activity, is warranted.
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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 | 0.000 | 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) | 0.001 | 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".