Bibliographic record
Abstract
PURPOSE OF REVIEW: Lung cancer is one of the major causes of cancer-related deaths. Grim mortality figures argue powerfully for new approaches to control this disease. Chemoprevention is the use of specific natural or synthetic chemical agents to reverse, suppress, or prevent carcinogenic progression to invasive cancer. The current article focuses on the field of lung cancer chemoprevention and recent advances. Lung cancer biology and general principles of prevention strategies are also described. RECENT FINDINGS: Trials in lung cancer chemoprevention have so far produced either neutral or harmful primary end point results whether in the primary, secondary and tertiary settings. The data suggest that lung cancer was not prevented by beta-carotene, alpha-tocopherol, retinal, retinyl palmitate, N-acetylcysteine, or isotretinoin in smokers. The results from the recently completed Canadian study of anethole dithiolethione in smokers with bronchial dysplasia as well secondary analyses of the phase III trials involving selenium and data from the US Intergroup NCI-91-0001 supporting treatment with isotretinoin in never and former smokers are hopeful and may help define new avenues of chemopreventive treatment after scientists and clinicians analyze the information generated. SUMMARY: The concept of chemoprevention in lung cancer is still in its infancy but one day may have a significant impact on the incidence and mortality of this leading cancer threat. An improved understanding of carcinogenesis and cancer prevention mechanisms will no doubt aid in the design of future clinical trials and in the validation of candidate agents as well as the development of new targets. Planned or ongoing trials currently are targeting important molecular markers of lung carcinogenesis and progression including cyclooxygenase-2, the ras-signaling pathway through farnesyl transferase inhibitors and the tyrosine kinase/epidermal growth factor receptor pathway. Until such studies are completed however, no drug or drug combination should be used for lung cancer prevention outside of a clinical study.
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 | 0.000 | 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 | 0.000 |
| 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".