Risk-Targeted Lung Cancer Screening
Bibliographic record
Abstract
Letters7 August 2018Risk-Targeted Lung Cancer ScreeningSonya Cressman, PhD, MBA, Kevin ten Haaf, MSc, Stephen Lam, MD, and Martin Tammemägi, DVM, MSc, PhDSonya Cressman, PhD, MBAThe British Columbia Cancer Agency, Vancouver, British Columbia, Canada (S.C., S.L.)Search for more papers by this author, Kevin ten Haaf, MScErasmus University Medical Center, Rotterdam, the Netherlands (K.T.)Search for more papers by this author, Stephen Lam, MDThe British Columbia Cancer Agency, Vancouver, British Columbia, Canada (S.C., S.L.)Search for more papers by this author, and Martin Tammemägi, DVM, MSc, PhDBrock University, St. Catharines, Ontario, Canada (M.T.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/L18-0236 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR:Kumar and colleagues (1) conducted a cost-effectiveness analysis (CEA) comparing lung cancer screening using risk-based criteria versus criteria based on NLST (National Lung Screening Trial) enrollment. They concluded, “Our analysis suggests that the gains from such risk-based eligibility likely would be small,” implying approximate equivalence between these criteria. A critical limitation of their analysis is that their sample was limited to NLST participants and does not represent the general population of smokers. Of note, they did not evaluate smokers who met risk-based but not NLST criteria. Consider using NLST criteria and the Prostate, Lung, Colorectal, and Ovarian ...References1. Kumar V, Cohen JT, van Klaveren D, Soeteman DI, Wong JB, Neumann PJ, et al. Risk-targeted lung cancer screening: a cost-effectiveness analysis. Ann Intern Med. 2018;168:161-9. [PMID: 29297005]. doi:10.7326/M17-1401 LinkGoogle Scholar2. Tammemägi MC, Katki HA, Hocking WG, Church TR, Caporaso N, Kvale PA, et al. Selection criteria for lung-cancer screening. N Engl J Med. 2013;368:728-36. [PMID: 23425165] doi:10.1056/NEJMoa1211776 CrossrefMedlineGoogle Scholar3. Tammemägi MC, Church TR, Hocking WG, Silvestri GA, Kvale PA, Riley TL, et al. Evaluation of the lung cancer risks at which to screen ever- and never-smokers: screening rules applied to the PLCO and NLST cohorts. PLoS Med. 2014;11:e1001764. [PMID: 25460915] doi:10.1371/journal.pmed.1001764 CrossrefMedlineGoogle Scholar4. Cressman S, Peacock SJ, Tammemägi MC, Evans WK, Leighl NB, Goffin JR, et al. The cost-effectiveness of high-risk lung cancer screening and drivers of program efficiency. J Thorac Oncol. 2017;12:1210-22. [PMID: 28499861] doi:10.1016/j.jtho.2017.04.021 CrossrefMedlineGoogle Scholar5. Ten Haaf K, Tammemägi MC, Bondy SJ, van der Aalst CM, Gu S, McGregor SE, et al. Performance and cost-effectiveness of computed tomography lung cancer screening scenarios in a population-based setting: a microsimulation modeling analysis in ontario, canada. PLoS Med. 2017;14:e1002225. [PMID: 28170394] doi:10.1371/journal.pmed.1002225 CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: The British Columbia Cancer Agency, Vancouver, British Columbia, Canada (S.C., S.L.)Erasmus University Medical Center, Rotterdam, the Netherlands (K.T.)Brock University, St. Catharines, Ontario, Canada (M.T.)Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=L18-0236. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoRisk-Targeted Lung Cancer Screening Vaibhav Kumar , Joshua T. Cohen , David van Klaveren , Djøra I. Soeteman , John B. Wong , Peter J. Neumann , and David M. Kent Risk-Targeted Lung Cancer Screening Vaibhav Kumar , Joshua T. Cohen , Peter J. Neumann , and David M. Kent Metrics Cited byLung Cancer Screening by Low-Dose Computed Tomography: Part 2 – Key Elements for Programmatic Implementation of Lung Cancer ScreeningRisk prediction models versus simplified selection criteria to determine eligibility for lung cancer screening: an analysis of German federal-wide survey and incidence data 7 August 2018Volume 169, Issue 3Page: 199-200KeywordsCancer screeningComputed axial tomographyCost effectiveness analysisDisclosureHealth economicsLung and intrathoracic tumorsLung cancer screeningPrevention, policy, and public healthPublic policySocioeconomic status ePublished: 7 August 2018 Issue Published: 7 August 2018 Copyright & PermissionsCopyright © 2018 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.011 |
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".