Opportunities and Challenges of Kava in Lung Cancer Prevention
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
Lung cancer is the leading cause of cancer-related deaths due to its high incidence, late diagnosis, and limited success in clinical treatment. Prevention therefore is critical to help improve lung cancer management. Although tobacco control and tobacco cessation are effective strategies for lung cancer prevention, the numbers of current and former smokers in the USA and globally are not expected to decrease significantly in the near future. Chemoprevention and interception are needed to help high-risk individuals reduce their lung cancer risk or delay lung cancer development. This article will review the epidemiological data, pre-clinical animal data, and limited clinical data that support the potential of kava in reducing human lung cancer risk via its holistic polypharmacological effects. To facilitate its future clinical translation, advanced knowledge is needed with respect to its mechanisms of action and the development of mechanism-based non-invasive biomarkers in addition to safety and efficacy in more clinically relevant animal models.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 it