Tattoos and Hematologic Malignancies in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Tattoos may cause a variety of adverse reactions in the body, including immune reactions and infections. However, it is unknown whether tattoos may increase the risk of lymphatic cancers such as non-Hodgkin lymphoma (NHL) and multiple myeloma. METHODS: Participants from two population-based case-control studies were included in logistic regression models to examine the association between tattoos and risk of NHL and multiple myeloma. RESULTS: A total of 1,518 participants from the NHL study (737 cases) and 742 participants from the multiple myeloma study (373 cases) were included in the analyses. No statistically significant associations were found between tattoos and risk of NHL or multiple myeloma after adjusting for age, sex, ethnicity, education, body mass index, and family history. CONCLUSIONS: We did not identify any significant associations between tattoos and risk of multiple myeloma, NHL, or NHL subtypes in these studies. IMPACT: Though biologically plausible, tattoos were not associated with increased risk of NHL or multiple myeloma in this study. Future studies with greater detail regarding tattoo exposure may provide further insights.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".