Bibliographic record
Abstract
Introduction Smokers are at risk for the chronic diseases, and they experience increased risk of road traffic crash (RTC), compared to non-smokers. Waterpipe smoking is sort of tobacco use with an estimated 100 million daily smokers through the world including the USA, UK, Australia, Germany, Canada and Middle East. The purpose of this research was to examine whether waterpipe smokers experience increased risk of RTC. Methods A telephone survey was arranged over a random sample of Iranian drivers. Using a Poisson regression analysis, the association of number of RTC during the last year and the drivers' characteristics (age, gender, diabetes, cigarette or waterpipe smoking, average daily drive time [DDT]) and vehicle characteristics (vehicle age, antilock braking systems) was evaluated. Results A total of 2070 drivers were included. 14.9% reported at least one RTC during the last year. There was significant association between number of RTCs and DDT, cigarette smoking and waterpipe smoking after adjustment for other variables. The association of RTC with waterpipe smoking was stronger than cigarette smoking. The prevalence of RTC in drivers who reported smoking cigarette and waterpipe was more than those who reported only waterpipe smoking which was itself more than those who smoked only cigarette. Conclusion Our study is among the first to show the association between waterpipe smoking and the risk of RTC, yet the mechanisms of actions need to be studied further. Public health initiatives to increase awareness on harms of waterpipe smoking among male youngsters can decrease the burden of RTC.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.007 | 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".