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Comorbidity Profiles and Lung Cancer Screening among Older Adults: U.S. Behavioral Risk Factor Surveillance System 2017–2019

2021· article· en· W3157526558 on OpenAlexaff
Shailesh Advani, Dongyu Zhang, Martin C. Tammemägi, Tomi Akinyemiju, Michael K. Gould, Gerard A. Silvestri, Dejana Braithwaite

Bibliographic record

VenueAnnals of the American Thoracic Society · 2021
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsBrock University
FundersNational Cancer Institute
KeywordsMedicineComorbidityBehavioral Risk Factor Surveillance SystemLung cancerRisk factorBehavioral riskGerontologyInternal medicineOncologyEnvironmental healthPathologyPublic health

Abstract

fetched live from OpenAlex

Abstract Rationale Although lung cancer screening (LCS) with low-dose computed tomography (LDCT) is now recommended for those meeting standard risk factor–based eligibility criteria, the role of comorbidity in the uptake of LCS with LDCT in an older real-world U.S. population is not well established. Objectives To examine the relationships between comorbidity, functional status, and LCS utilization in the United States. Methods Using population-based data from the 2017–2019 Behavioral Risk Factor Surveillance System, we examined the association of comorbid conditions and functional limitations regarding activities of daily living with LCS utilization among participants that met the LCS criteria based on the U.S. Preventive Service Taskforce guidelines. We employed multivariable weighted logistic regression models to evaluate these associations, both overall and within subgroups defined by age (<65 yr vs. ⩾65 yr), sex, and smoking history. Results Of 11,214 participants who met the eligibility criteria for LCS, 1,731 (16%) underwent LCS with LDCT. The majority were White (90%), male (55%), former smokers (52%), and living with at least one chronic comorbid condition (77%). More than 28% had three or more comorbid conditions, and approximately 40% of participants reported having some form of functional limitations. In the multivariable models, the likelihood of undergoing LCS with LDCT within the past year was positively associated with higher amount of comorbidity (⩾5 vs. 0: adjusted odds ratio, 2.34; 95% confidence interval [CI], 1.22–4.48) but not with functional limitations (⩾3 vs. 0: adjusted odds ratio, 1.00; 95% CI, 0.66–1.50). Conclusions The presence of comorbid conditions is associated with a higher likelihood of undergoing LCS with LDCT. Because poor health status may diminish the benefits of screening, future research is needed to precisely characterize the health status of LCS-eligible individuals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.054
GPT teacher head0.396
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations27
Published2021
Admission routes1
Has abstractyes

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