A Study of Multiple Causes of Death in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate rheumatoid arthritis (RA)-related mortality in the state of São Paulo (Brazil). METHODS: Data from all death certificates (DC) from 1996 to 2010 were analyzed using a multiple cause-of-death method. We compared the results from 2 subperiods (1996-2000 and 2006-2010). RESULTS: We found 3955 DC related to RA - 27.6% with RA as the underlying cause of death (UCD) and 72.4% with RA as the nonunderlying cause of death (NUCD). Ninety percent of RA-related deaths occurred at age ≥ 50 years. The mean ages at death were 67.1 ± 13.3 and 67.9 ± 13 years for RA as the UCD and NUCD, respectively. The most frequent NUCD associated with RA were pneumonia, sepsis, renal failure, interstitial lung disease, and heart failure. In the last subperiod, there was an increase in infectious causes. When RA was an NUCD, we observed a decrease in the mean age at death for the last subperiod (p = 0.021). The most common UCD were circulatory and respiratory system diseases. Comparing the mean age at death between RA-related deaths and the general population when deaths occurred at ages beyond 50 years, the linear regression analysis showed a downward curve for RA-related death (p < 0.001 and r = -0.795), while for the general population, as expected, the curve had an upward pattern (p < 0.001 and r = 0.993). CONCLUSION: Unexpectedly, RA-related deaths occurred at earlier ages in the more recent subperiod. Cardiovascular disease remained the most important cause, and infectious diseases are an increasing cause of death associated with RA, raising the question of whether infections were related to the more vigorous immunosuppressive treatment recommended by recent guidelines.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".