Impact of Comorbidities on the Mortality Benefits of Lung Cancer Screening: A Post-Hoc Analysis of the PLCO and NLST Trials
Bibliographic record
Abstract
OBJECTIVES: To evaluate how comorbidities affect mortality benefits of lung cancer screening (LCS) with low-dose computed tomography. METHODS: We developed a comorbidity index (Prostate, Lung, Colorectal, and Ovarian comorbidity index [PLCO-ci]) using LCS-eligible participants' data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) trial (training set) and the National Lung Screening Trial (NLST) (validation set). PLCO-ci predicts five-year non-lung cancer (LC) mortality using a regularized Cox model; with performance evaluated using the area under the receiver operating characteristics curve. In NLST, LC mortality (per original publication) was compared between low-dose computed tomography and chest radiograph arms across the PLCO-ci quintile (Q1-5) using a cause-specific hazard ratio (csHR) with 95% confidence intervals (CIs). RESULTS: Analyses included 34,690 PLCO and 53,452 NLST participants (mean age: 62 y [±5 y] and 61 y [±5 y], 58% and 59% male individuals, and 39% and 41% active smokers, respectively). PLCO-ci predicted five-year non-LC mortality with an area under the receiver operating characteristics curve of 0.72 (95% CI: 0.71-0.74) in PLCO and 0.69 (95% CI: 0.67-0.70) in NLST. In NLST, at a median follow-up of 6.5 years, LC mortality was significantly reduced for participants with intermediate comorbidity (Q2, Q3, and Q4): csHR 0.62 (95% CI: 0.41-0.95), 0.68 (95% CI: 0.48-0.96), and 0.72 (95% CI: 0.54-0.96) respectively, with a nonstatistically significant reduction for Q1 (csHR = 0.72, 95% CI: 0.45-1.17) and no reduction for Q5 participants (csHR = 0.99, 95% CI: 0.79-1.23). Participants in Q2, Q3, and Q4 (60%) accounted for 89% of LC deaths averted among all NLST participants. Q1 participants had low LC incidence, whereas Q5 had higher localized LC lethality, more squamous cell carcinomas, and untreated LC. CONCLUSIONS: The PLCO-ci developed in this work shows that individuals with intermediate comorbidity benefited the most from LCS, highlighting the need of addressing comorbidities to achieve LC mortality benefits.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.018 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".