Polymyositis/dermatomyositis and Malignancy Risk: A Metaanalysis Study
Bibliographic record
Abstract
OBJECTIVE: To investigate the association between polymyositis (PM)/dermatomyositis (DM) and risks of malignancy. METHODS: We searched Pubmed for articles dated before August 16, 2013. Studies were included if they met the following criteria: (1) a cohort or observational study; (2) PM or DM as one of the exposures of interest; (3) cancer as an outcome of interest; and (4) the rate ratio (RR) or standardized incidence ratio (SIR) were available with their 95% CI. We used random-effects or fixed-effects models to calculate the pooled RR according to the heterogeneity test. RESULTS: Twenty publications were included. Compared with the general population, the pooled RR for patients with PM, DM, and PM/DM were 1.62 (95% CI 1.19-2.04), 5.50 (4.31-6.70), and 4.07 (3.02-5.12), respectively. The increased risks were more significant in patients within the first year of myositis diagnosis, male patients, and population-based studies (for DM). A significant association was also found between PM or DM and most site-specific malignancies. However, both PM and DM were not associated with stomach and prostate cancers. Significant heterogeneity was found between studies on association between PM/DM and overall malignancy, but not between PM/DM and the majority of site-specific malignancies, suggesting that that inherent malignancy difference may be a major source of heterogeneity. CONCLUSION: The present metaanalysis indicates that PM and DM are significantly associated with increased risks of overall malignancy and most site-specific malignancies. The number of studies on association between PM or DM and some malignancies is too small to draw a firm conclusion. Accordingly, more research is needed for these malignancies.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.044 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".