Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Lung cancer is one of the major causes of cancer-related deaths. Lung cancer mortality figures argue powerfully for new approaches to control this disease. The term chemoprevention can be defined as the use of specific natural or synthetic chemical agents to reverse, suppress, or prevent premalignancy from progressing to invasive cancer. METHODS: Issues related to lung cancer chemoprevention are reviewed, including risk factors and identification of high-risk cohorts, endpoint biomarkers, and current and new chemopreventive agents. Also, important findings from chemoprevention randomized, controlled trials are summarized. RESULTS: Trials in lung cancer chemoprevention have so far produced either neutral or harmful primary endpoint results, whether in the primary, secondary, or tertiary settings. Lung cancer was not prevented by beta-carotene, alpha-tocopherol, retinol, retinyl palmitate, N-acetylcysteine, or isotretinoin in smokers. Secondary results from the phase III trials involving selenium and vitamin E, as well as results from the US Intergroup NCI I91-0001 trial supporting treatment with isotretinoin in never and former smokers, are promising and may help define new avenues for chemoprevention. CONCLUSIONS: 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. Molecular markets of risk, drug activity and targeting, improved imaging techniques, and new drug delivery systems are being evaluated.
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.
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 | 0.001 | 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 it