Bibliographic record
Abstract
The current opioid epidemic is an unequivocal and serious public health issue. Drug overdoses are now the leading cause of death for Americans under 50 years old, with most of the fatalities due to opioid overdoses. However, few studies have focused on the carcinogenic effects of long-term opium consumption. Moossavi and colleagues examined the association between opium consumption and pancreatic cancer incidence in a large-scale prospective cohort in Iran. This long-term case–control study confirmed opium carcinogenicity and revealed chronic opium use as a risk factor for pancreatic cancer. More studies should be organized to assess the carcinogenicity of long-term opioid use.International and ethnic differences in cancer rates exist, and they are minimally explained by genetic factors. A substantial portion of the differences in cancer rates can be explained by modifiable factors, while other factors, such as the microbiome and the metabolome, are emerging as important intermediary components in cancer prevention. To refine current concepts, researchers have started incorporating emerging technologies for measuring diet and physical activity in human populations. Questions remain regarding reported associations and the best methods to assess associations. This position paper from Mahabir and colleagues highlights the state of the science and priorities for future research.Routine testing of colorectal cancer (CRC) starting at age 50 saves lives, but local CRC screening data are not widely available. Multilevel regression post-stratification for small-area estimation with 2014 Behavioral Risk Factor Surveillance System and Census 2014 county population data revealed substantial county-level variations in the prevalence of being up-to-date with CRC screening: it ranged from 40.1% to 79.8%, with a median of 65.5%. More than 80% of 3,142 counties had <70% up-to-date screening, far from the national goal of 80% by 2018. This study by Berkowitz and colleagues suggests a great need for locally targeted interventions for counties with very low CRC screening prevalence.Tobacco-specific nitrosamines (TSNAs) are carcinogens found in tobacco products. Recent evidence indicates that levels of the TSNA NNK have increased in Canadian cigarettes. The current study by Czoli and Hammond examined whether these increases translated into differences in exposure among Canadian tobacco users, using urinary metabolite measures from the Canadian Health Measures Survey. The findings indicate that exposure to the TSNA NNK among Canadian tobacco users increased considerably from 2007–09 through 2012–13, in parallel to NNK increases in the tobacco of Canadian cigarettes. The findings raise questions about the commitment of tobacco manufacturers to minimize carcinogen levels to the full extent possible.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.279 | 0.135 |
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".