Bibliographic record
Abstract
PURPOSE OF REVIEW: Advances in imaging technologies and biomarker research offer hope that the incidence and mortality of lung cancer can be reduced by screening similar to what have been achieved for cancer of the cervix, breast, and colon. RECENT FINDINGS: Spiral computed tomography with multitrack scanners and autofluorescence bronchoscopy offer unprecedented sensitivity to detect lung cancer even during the preinvasive stage. The high sensitivity of these tests, however, is associated with a low specificity. Better selection of individuals at highest risk of lung cancer using biomarkers in sputum, blood, or exhaled breath, as well as a better understanding of genetic susceptibility, may improve their positive predictive values, minimize unnecessary downstream investigations or treatment, as well as reduce screening costs. SUMMARY: Improvement in the performance of sputum, exhaled breath, or blood biomarkers holds promise as the first screening step to identify individuals at highest risk of lung cancer beyond what age and smoking could predict to select those who would obtain the most benefits from spiral computed tomography or autofluorescence bronchoscopy as localization tools.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".