Early Lung Cancer Action Project: A Summary of the Findings on Baseline Screening
Bibliographic record
Abstract
PURPOSE: The Early Lung Cancer Action Project (ELCAP) is designed to evaluate baseline and annual repeat screening by low radiation dose computed tomography (low-dose CT) in persons at high-risk for lung cancer. METHODS: Since starting in 1993, the ELCAP has enrolled 1,000 asymptomatic persons, 60 years of age or older, with at least 10 pack-years (1 pack per day for 10 years, or 2 packs per day for 5 years) of cigarette smoking, no prior cancer, and medically fit to undergo thoracic surgery. After a structured interview and informed consent, baseline chest radiographs and low-dose CT were obtained on each subject. The diagnostic work-up of screen-detected noncalcified pulmonary nodules (NCN) was guided by ELCAP recommendations which included short-term high-resolution CT follow-up for the smallest nodules. Baseline RESULTS: On low-dose CT at baseline compared to chest radiography, NCN were detected three times as commonly (23% versus 7%), malignancies four times as commonly (2.7% versus 0.7%), and stage I malignancies six times as commonly (2.3% versus 0.4%). Of the 27 CT-detected cancers, 96% (26/27) were resectable; 85% (23/27) were stage I, and 83% (19 of the 23 stage I) were not seen on chest radiography. Following the ELCAP recommendations, biopsies were performed on 28 of the 233 subjects with NCN; 27 had a malignant and one a benign NCN. Another three individuals underwent biopsy outside of the ELCAP recommendations; all had benign NCNS: No one had thoracotomy for a benign nodule. CONCLUSION: Baseline CT screening for lung cancer provides for detecting the disease at earlier and presumably more commonly curable stages in a cost-effective manner.
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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 | 0.025 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".