Mortality and Incidence of Malignancy in Korean Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To determine the standardized mortality ratio (SMR) and standardized incidence ratio (SIR) for malignancy in Korean patients with rheumatoid arthritis (RA). METHODS: We enrolled 1534 patients with RA who fulfilled the American College of Rheumatology criteria, from October 2001 to December 2007. Baseline assessment included sociodemographic variables, laboratory findings including rheumatoid factor, anticitrullinated protein antibody, functional class, radiological stage, medication, and the Korean version of the Health Assessment Questionnaire. We used the national mortality rate from 2001 to 2007 from the Korean National Statistical Office (KNSO) and the incidence rate from the Korean Central Cancer Registry (KCCR) from 2001 to 2007 as comparison data for estimates of SMR and SIR. Confidence intervals were calculated based on the Poisson distribution. RESULTS: There were 57 deaths in 6683 person-years of followup. The number of expected deaths (derived from the KNSO) was 42.33 and the SMR for patients with RA was 1.35 (95% CI 1.02-1.74). The main causes of death were malignancy, cardiovascular disease, and respiratory disease. In the cause-specific SMR, deaths from respiratory disease, especially from interstitial lung disease (ILD) and pneumonia, were significantly higher than expected: 4.66 (95% CI 2.13-8.85) for all respiratory disease, 18.18 (95% CI 2.20-65.64) for ILD, and 10.26 (95% CI 2.79-26.26) for pneumonia. Thirty malignancies had occurred in 1501 patients. The number of expected malignancies derived from the KCCR was 34.91, yielding a SIR for cancer of 0.86 (95% CI 0.58-1.23). CONCLUSION: Our study demonstrates that the SMR was slightly higher in patients with RA, but the incidence rates of malignancies were not significantly different from the general population. But deaths from respiratory diseases were significantly higher.
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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 | 0.001 |
| 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".