Risk of Malignancy in Dermatomyositis and Polymyositis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: There is variation in the risk of malignancy in dermatomyositis (DM) and polymyositis (PM) in the existing literature. OBJECTIVE: To conduct a meta-analysis to estimate the risk of malignancy in DM and PM as compared with the general population. METHODS: Medline and Embase Database abstracts were searched through August 2014 using the search terms myositis, neoplasms, and paraneoplastic syndromes. Population-based, observational studies in English were included. Meta-analyses were conducted using random-effects models. RESULTS: A total of 5 studies with 4538 DM or PM patients were included in the analysis. The overall relative risk was 4.66 for DM and 1.75 for PM. By gender, the standardized incidence ratio (SIR) of malignancy among DM patients was 5.29 for males and 4.56 for females; the SIR of malignancy among PM patients was 1.62 for males and 2.02 for females. By time since diagnosis, the SIR of malignancy among DM patients was 17.29 in the first year, 2.7 between 1 and 5 years, and 1.37 after 5 years. By age group, the SIR among DM patients was 2.79 for patients between 15 and 44 years and 3.13 beyond 45 years. CONCLUSIONS: Both DM and PM are associated with increased risk of malignancy, but the risk is higher in DM. The risk of malignancy is present in both genders and all age groups and is highest in the first year after diagnosis but persists beyond the fifth year in DM. Adults should be evaluated for malignancy at diagnosis, followed by long-term surveillance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it