Malignancy incidence in 5294 patients with juvenile arthritis
Bibliographic record
Abstract
OBJECTIVE: To determine cancer incidence in a large clinical juvenile-onset arthritis population. METHODS: We combined data from 6 existing North American juvenile-onset arthritis cohorts. Patients with juvenile-onset arthritis were linked to regional cancer registries to detect incident cancers after cohort entry, defined as first date seen in the paediatric rheumatology clinic. The expected number of malignancies was obtained by multiplying the person-years observed (defined from cohort entry to end of follow-up) by the geographically matched age, sex and calendar year-specific cancer rates. The standardised incidence ratios (SIR; ratio of cancers observed to expected) were generated, with 95% CIs. RESULTS: The 6 juvenile arthritis registries provided a total of 5294 patients. The mean age at cohort entry was 8.9 (SD 5.0) years and 68% of participants were female. The mean duration of follow-up was 6.8 years with a total of 36 063 person-years spanning 1978-2012. During follow-up, 9 invasive cancers occurred, compared with 10.9 expected (SIR 0.82, 95% CI 0.38 to 1.5). 3 of these were haematological (Hodgkin's, non-Hodgkin's lymphoma and leukaemia). 6 of the patients with cancer were exposed to disease-modifying drugs; 5 of these had also been exposed to biological agents. CONCLUSIONS: We did not clearly demonstrate an increase in overall malignancy risk in patients with juvenile-onset arthritis followed for an average of almost 7 years. 3 of the 9 observed cancers were haematological. 5 of the cancers arose in children exposed to biological agents. Longer follow-up of this population is warranted, with further study of drug effects.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".