Bibliographic record
Abstract
Thank you for printing the outstanding collection of articles about tobacco and health disparities in the February 2004 issue of the Journal. Although most of tobacco’s deleterious health effects are long-term, smoking materials remain the leading cause of fatal fires in the United States, causing roughly one quarter of home fire deaths. One study compared the demographic characteristics of smokers whose cigarettes had started a fire with those of smokers who had not had a fire. Households with incomes of less than $10 000 accounted for 45.6% of smokers who had had fires and only 16.6% of smokers who had not. Smokers who were not high school graduates accounted for 38.3% of smokers who had had fires and only 18.4% of those who had not.1 Not surprisingly, states with larger percentages of adults lacking high school diplomas and households below the poverty level tend to have higher fire death rates.2 Barbeau et al. showed that these factors are also correlated with smoking.3 Smoking bans protect health, but they also protect property. Fee and Brown described the positive effect that hospital smoking bans had on the hospital environment and the smoking behavior of hospital employees.4 Hospital fires started by smoking materials fell by 96%, from 3200 in 1980 to 130 in 1998. In 1980, these fires accounted for 40% of hospital fires. In 1998, the figure was only 8% (Ahrens M, unpublished data, 2004). I had to do a mental shift when reading Fairchild and Colgrove’s article on the “safer” cigarette.5 The fire safety community is strenuously advocating legislative requirements for self-extinguishing cigarettes. While such cigarettes would not eliminate all cigarette fires, they would substantially reduce the number of fires caused by smoldering cigarettes. The National Fire Protection Association is in the harm reduction camp. Until smokers quit, we want them to use a product with reduced risk. Because quitting often takes numerous attempts, we advocate cigarettes that are less prone to ignition, products that are less flammable, and education about smoking practices that are less likely to start fires. Difficult questions arise when someone receiving supplemental medical oxygen continues to smoke. The Joint Commission on Accreditation of Healthcare Organizations discussed the root causes of 11 fires involving home medical oxygen in the article “Lessons Learned: Fires in the Home Care Setting.”6 Many caregivers, agencies, and fire departments are struggling with this issue. Smoking is a significant part of the fire and injury problem. Preventing smoking will prevent fire deaths. We applaud the efforts taken to identify the most effective strategies and hope that Journal readers will consider including fire prevention in their programs.
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 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".