Identifying high risk individuals for targeted lung cancer screening: Independent validation of the PLCO<sub>m2012</sub> risk prediction tool
Bibliographic record
Abstract
Lung cancer screening with computerised tomography holds promise, but optimising the balance of benefits and harms via selection of a high risk population is critical. PLCOm2012 is a logistic regression model based on U.S. data, incorporating sociodemographic and health factors, which predicts 6‐year lung cancer risk among ever‐smokers, and thus may better predict those who might benefit from screening than criteria based solely on age and smoking history. We aimed to validate the performance of PLCOm2012 in predicting lung cancer outcomes in a cohort of Australian smokers. Predicted risk of lung cancer was calculated using PLCOm2012 applied to baseline data from 95,882 ever‐smokers aged ≥45 years in the 45 and Up Study (2006–2009). Predictions were compared to lung cancer outcomes captured to June 2014 via linkage to population‐wide health databases; a total of 1,035 subsequent lung cancer diagnoses were identified. PLCOm2012 had good discrimination (area under the receiver‐operating‐characteristic‐curve; AUC 0.80, 95%CI 0.78–0.81) and excellent calibration (mean and 90th percentiles of absolute risk difference between observed and predicted outcomes: 0.006 and 0.016, respectively). Sensitivity (69.4%, 95%CI, 65.6–73.0%) of the PLCOm2012 criteria in the 55–74 year age group for predicting lung cancers was greater than that using criteria based on ≥30 pack‐years smoking and ≤15 years quit (57.3%, 53.3‐61.3%; p < 0.0001), but specificity was lower (72.0%, 71.7–72.4% versus 75.2%, 74.8–75.6%, respectively; p < 0.0001). Targeting high risk people for lung cancer screening using PLCOm2012 might improve the balance of benefits versus harms, and cost‐effectiveness of lung cancer 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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".