Bibliographic record
Abstract
Alcohol is a leading preventable cause of cancer death in the United States, according to a study published in the American Journal of Public Health.1 The researchers, from the Boston University School of Medicine and the Boston University School of Public Health in Massachusetts, also demonstrated that reducing alcohol consumption is a key cancer prevention strategy as, even in small amounts, alcohol is a known carcinogen. Alcohol has been implicated consistently in previous studies as a significant risk factor for cancers of the mouth, throat, esophagus, and liver. It also has been shown to increase the risk of cancers of the colon, rectum, and female breast. Estimates indicate that it accounts for approximately 4% of all cancer-related deaths worldwide. Researchers, including senior author Timothy Naimi, MD, MPH, of the Boston University School of Medicine and colleagues from the National Cancer Institute, the Alcohol Research Group at the Public Health Institute, and Canada's Centre for Addiction and Mental Health, examined US data on alcohol consumption and cancer mortality. They found that alcohol consumption resulted in approximately 20,000 cancer deaths annually, or approximately 3.5% of all US cancer deaths. Breast cancer was the most common cause of alcohol-related deaths in women, accounting for approximately 6000 deaths, or 15% of all breast cancers, annually. Cancers of the mouth, throat, and esophagus were common causes of alcohol deaths in men, accounting for approximately 6000 annual deaths. Dr. Naimi and his colleagues also determined that each alcoholrelated death led to approximately 18 years of potential life lost. Furthermore, although higher levels of alcohol consumption led to a higher cancer risk, an average consumption of 1.5 drinks per day or less accounted for 30% of all alcohol-related cancer deaths. Dr. Naimi adds that although the relationship between alcohol and cancer is strong, it is not widely appreciated by the public and is underemphasized by physicians. In a news release issued by Boston University, he calls alcohol “a big preventable cancer risk factor that has been hiding in plain sight.”
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".