Malignancies in the rheumatoid arthritis abatacept clinical development programme: an epidemiological assessment
Bibliographic record
Abstract
OBJECTIVE: To provide context for the malignancy experience in the rheumatoid arthritis (RA) abatacept clinical development programme (CDP) by performing comparisons with similar RA patients and the general population. METHODS: Malignancy outcomes included total malignancy (excluding non-melanoma skin cancer (NMSC)), breast, colorectal, lung cancers and lymphoma. Comparisons were made between the observed incidence in patients within the abatacept CDP and RA patients on disease-modifying antirheumatic drugs (DMARD) identified from five data sources: the population-based British Columbia RA Cohort, the Norfolk Arthritis Register, the National Data Bank for Rheumatic Diseases, the Sweden Early RA Register and the General Practice Research Database. Age and sex-adjusted incidence rates (IR) and standardised incidence ratios (SIR) were used to compare events in the abatacept trials with the RA DMARD cohorts and the general population. RESULTS: A total of 4134 RA patients treated with abatacept in seven trials and 41,529 DMARD-treated RA patients in the five observational cohorts was identified for study inclusion. In the abatacept-treated patients, the 51 malignancies (excluding NMSC), seven cases of breast, two cases of colorectal, 13 cases of lung cancer and five cases of lymphoma observed were not greater than the range of expected cases from the five RA cohorts. The SIR comparing RA patients with the general population were consistent with those reported in the literature. CONCLUSIONS: The IR of total malignancy (excluding NMSC), breast, colorectal, lung cancers and lymphoma in the abatacept CDP were consistent with those in a comparable RA population. These data suggest no new safety signals with respect to malignancies, which will continue to be monitored.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".