Variation in the efficacy of low-dose computed tomographic lung screening based on risk of lung cancer mortality in the National Lung Screening Trial.
Bibliographic record
Abstract
1523 Background: Low-dose computed tomography (LDCT) screening reduced lung cancer mortality by 20% in the National Lung Screening Trial (NLST). The efficacy of LDCT screening could be improved by targeting smokers at highest risk of lung cancer death, provided that the efficacy of LDCT screening increases with lung cancer mortality risk. Methods: We evaluated the efficacy of LDCT screening as compared to chest radiography in the NLST across groups defined by participants’ 5-year risk of lung cancer mortality at randomization, which was estimated using a validated prediction model. Across quintiles of 5-year lung cancer mortality risk [Q1: 0.15%-0.55%, Q2: 0.56%-0.84%, Q3: 0.85%-1.24%, Q4: 1.24%-2.0%, Q5: >2.0%], we estimated the number of participants with false positive screens, the number of prevented lung cancer deaths, and their ratio. Results: The number of prevented lung cancer deaths due to LDCT screening increased in tandem with lung cancer mortality risk (Q1=0.2, Q2=3.5, Q3=5.1, Q4=11.0, Q5=12.0 per 10,000 person-years; P-trend=0.01). The number of participants with false positive screens per lung cancer death prevented, a measure of screening efficiency, significantly decreased with increasing risk (Q1=1,648, Q2=181, Q3=147, Q4=64, Q5=65, P-trend<0.001). The 60% of participants at highest 5-year lung cancer mortality risk (0.85% or greater) accounted for 88% of LDCT-preventable lung cancer deaths and included only 64% of participants with a false positive screen. The 20% of participants at lowest lung cancer mortality risk (0.15%-0.55%) accounted for only 1% of LDCT-preventable lung cancer deaths. Conclusions: In the NLST, LDCT screening prevented the most lung cancer mortality among those at highest lung cancer mortality risk and prevented almost no mortality among those at lowest risk, providing empirical support for risk-based targeting of smokers to improve the efficacy of LDCT screening.
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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 | 0.086 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".