Tattoos and Hematologic Malignancies in British Columbia, Canada
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: 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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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