Nicotine dependence perpetuating tobacco smoking may be treatable by drugs acting at glutamate receptors.
Bibliographic record
Abstract
Extract: By the year 2020 tobacco smoking will become the single largest health problem worldwide leading to approximately 8.4 million deaths annually. In the USA alone, tobacco smoking leads to serious illness in an estimated 8.6 million people, causing roughly 440,000 deaths annually, and approximately $157 billion in health-related economic costs. On average, smoking leads to a loss of 12 healthy years and reduces the lifespan by 8 years. Almost a quarter of the USA population are tobacco smokers. The percentage of smokers is even higher in some other countries, and there is an alarming increase of tobacco use in the developing countries. The cost of tobacco smoking to society is tremendous in terms of health problems that frequently lead to death, medical costs and human suffering. In the USA and Europe, 70% of all smokers have considered quitting smoking at least once, and 35% try to quit at least once a year. Yet only approximately 6% succeed in maintaining abstinence. Therefore, there is a great need for the discovery and development of new treatments to assist people in achieving and maintaining abstinence from tobacco smoking.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".