Environmental Factors and Multiple Myeloma Risk: A Population-Based Retrospective Cohort Study in the United States
Bibliographic record
Abstract
Multiple myeloma (MM) is claimed to be a leading fatal cancer in the United States. A population-based retrospective cohort study was conducted to examine the relationship between the risk of multiple myeloma and environmental factors including CO2 intensity leaked by oil equivalent energy use, PM2.5, total greenhouse gas emission, and sanitized water usage, respectively, and to further determine any association's worldwide universality. We used multivariate unconditional analysis (ANOVA) to examine the distributions of MM incidence rate among groups with selected characteristics. In addition, we used multivariate conditioned generalized linear regression models to estimate effects of each environmental factor on MM incidence rate. A rate. Among black racial groups aged from 65 to 74 years in the United States, total greenhouse gas emission had a positive effect on the cancer risk as predicted. However, in accordance with empirical work to date, our comparative analyses revealed that MM incidence rate was not significantly associated with an increase in other in other variates relative to age. Thus, our combined results require further confirmation in other populations with specific personal information.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".