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Record W7115035865

Leveraging low-dose CT in lung cancer screening to improve chronic obstructive pulmonary disease diagnosis and treatment

2025· dissertation· en· W7115035865 on OpenAlexfundaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2025
Typedissertation
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsLung cancer screeningPulmonary diseaseLung cancerComputed tomographyCOPDDisease
DOInot available

Abstract

fetched live from OpenAlex

Abstract:Lung cancer screening (LCS) programs offer an opportunity to improve access to COPD diagnosis and treatment in high-risk individuals. COPD is one of the top three causes of death globally and remains underdiagnosed due to its heterogeneous clinical presentation, with most patients diagnosed at moderate to severe stages. LCS-eligible individuals, typically smokers or ex-smokers, are at high risk of COPD. Markers such as emphysema and airway thickening can be identified on low-dose CT (LDCT), presenting an opportunity to use these scans to increase early COPD diagnosis and reduce mortality.Objectives:To identify the proportion of untreated individuals with symptomatic COPD—defined as radiographic emphysema on LDCT plus dyspnea and/or elevated CAT score—and to explore factors associated with receiving COPD pharmacotherapy among LCS participants.Methods: Data were analyzed from participants recruited through the Quebec LCS program between April 2023 and July 2024 across 10 hospitals. The study included smokers or ex-smokers aged 55–74 with a PLCOm2012 ≥ 2% and a Lung-RADS 1–2 baseline LDCT. Radiographic COPD was defined as emphysema identified on LDCT by a thoracic radiologist, combined with dyspnea and COPD symptoms (mMRC ≥ 1) and/or CAT score ≥ 10. The primary outcome was having COPD pharmacotherapy (long-acting bronchodilators). Secondary outcomes included COPD symptoms, health literacy and quality of life. Logistic regression identified factors associated with treatment, adjusting for emphysema severity, smoking status, BMI, and sociodemographic factors. Gender-based differences were explored through stratified analysis, and BMI was evaluated in relation to symptoms and emphysema severity.Results: Among 1026 participants with Lung-RADS 1–2 LDCT, 801 had visual emphysema. Most (603/801; 75%) were untreated or only using short-acting beta agonists. Of these, 66% (397/603) were screened for dyspnea and COPD symptoms, and 56% (221/397) were found symptomatic (mean CAT 12.6, mMRC 0.91). Women (111/174; 64%) reported more dyspnea and COPD symptoms than men (110/223; 49%), particularly in minimal-to-mild emphysema (mean CAT 10.2 vs. 8.2, p < 0.05).Adjusted analyses showed that treatment with long-acting bronchodilators was associated with moderate-to-severe emphysema (OR 2.35; 95% CI 1.49–3.70), lower education (OR 0.58; 95% CI 0.37–0.91), and female sex (OR 1.58; 95% CI 1.04–2.42). Symptom burden increased with BMI category, although underweight and normal-weight participants had similar proportions of symptoms (50% and 49%, respectively), and no significant mMRC differences were observed between BMI groups. Pack-years increased with BMI. The likelihood of treatment increased with emphysema severity in normal (OR 1.84; 95% CI 0.90–3.74), overweight (OR 2.46; 95% CI 1.18–5.13), and obese (OR 4.06; 95% CI 1.21–13.6) categories. Lower education (OR 0.46; 95% CI 0.22–0.98) in normal weight and ex-smoking status (OR 0.45; 95% CI 0.23–0.89) in the overweight group were also associated with increased treatment likelihood. The underweight group was too small for model convergence.Conclusion: Untreated radiographic COPD with significant symptoms was frequent in this LCS cohort. Treatment was associated with female sex and moderate-to-severe emphysema. LDCT-reported emphysema and symptom burden should be considered by clinicians when reviewing LCS results. Integrating COPD diagnosis into LCS programs presents a key opportunity to improve early detection and management of COPD in a high-risk population

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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)0.002
metaresearch head score (Gemma)0.006
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 score0.200
Threshold uncertainty score0.398

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.015
GPT teacher head0.287
Teacher spread0.272 · 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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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