Concordance of a Web-based Lung Cancer Risk Self-Assessment Tool With Nursing Risk Assessment
Bibliographic record
Abstract
Background: The Alberta Lung Cancer Screening Program implemented a web-based risk self-assessment tool using the Prostate, Lung, Colorectal, Ovarian Model 2012 (PLCOm2012) model. To determine the accuracy of individual self-risk assessments, this study compared user online entries with program nurse assessments. Research Question: Is the web-based lung cancer risk self-assessment tool based on the PLCOm2012 model accurate in predicting lung cancer risk among Albertans aged 50 to 74 years when incorporated into an existing lung cancer screening program? Study Design and Methods: This retrospective study used administrative data from the Alberta Lung Cancer Screening Program, which was designed to enroll 3,800 participants from September 2022 to September 2024. All self-referrals from the web tool with a risk score ≥ 1.5% were included up to July 2024. Concordance was assessed between web entries and matched nurse assessments and the impact of discordant lung cancer risks estimated. Result: A total of 384 matched entries were analyzed. The mean age of the participants was 64 ± 6 years, and most were currently smoking (61%). The study revealed high (> 95%) concordance between web entries and nurse assessments; concordance for education, smoking intensity, and smoking duration were slightly lower (90%-92%). Although a discrepancy in at least 1 value was common (32%), this resulted in an eligible participant being re-categorized as ineligible only in 3.7% of cases. Overall, 65% of participants had concordant PLCOm2012 risk scores (within 0.1%). The differences in PLCOm2012 risk between web and nurse entries ranged from - 3% to 2.5%. Interpretation: This study described the feasibility of implementing a web-based lung cancer risk self-assessment tool based on the PLCOm2012 model and found a high concordance of discrete data points between the web entries and nurse entries. However, at least 1 value was discordant in one-third of the participants, and this occasionally affected risk scores. Confirmation of lung cancer risk by a health care provider remains important prior to 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 | 0.022 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".