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Record W4412170658 · doi:10.2196/76515

Adherence to Posttreatment Surveillance Guidelines in Non–Small Cell Lung Cancer: Retrospective Cohort Study

2025· article· en· W4412170658 on OpenAlexvenueno aff
Ryan J Randle, Scott V. Adams, Zahra Esfahani‐Monfared, Nicole Lin, Julie Wu, Ann N. Leung, Steven M. Asch, Steven B. Zeliadt, Alex H. S. Harris, Summer S. Han, Leah M. Backhus

Bibliographic record

VenueJMIR Cancer · 2025
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsnot available
FundersU.S. Department of Veterans Affairs
KeywordsPreprintMedicineLung cancerAdministration (probate law)CohortCohort studyFamily medicineEnvironmental healthOncologyInternal medicinePolitical science

Abstract

fetched live from OpenAlex

Background: Several guidelines recommend posttreatment surveillance for non-small cell lung cancer (NSCLC). However, studies evaluating surveillance patterns often cannot distinguish between imaging ordered for surveillance versus for symptoms suggestive of recurrence. Moreover, early recurrences and other competing events hamper efforts to determine true surveillance rates because of wide variability in reported guideline adherence in clinical practice. Leveraging comprehensive Veterans Health Administration data, we developed a novel competing risks framework to describe the patterns and predictors of NSCLC imaging surveillance. Objective: This study aims to examine posttreatment surveillance to estimate the true surveillance rates and predictors of guideline-concordant care in patients with early-stage NSCLC. Methods: The study cohort comprised veterans who were treated for stage 1 to 3 NSCLC between 2008 and 2016 and who survived for ≥6 months. Clinical documents and radiology reports were abstracted for image indication and clinical information. We estimated the cumulative probability of receiving guideline-concordant surveillance, defined as chest computed tomography imaging within 4 to 9 months after treatment, accounting for competing risks and censoring. Multivariable cause-specific Cox regression was used to estimate associations between patient factors and guideline-concordant surveillance, with adjustments made for multiple comparisons. Results: The cohort consisted of 1888 patients. The mean age of the analysis cohort was 66.4 (SD 7.9) years; 95.9% (1811/1888) of the patients were male, 71.1% (1342/1888) of the patients were White, and 43.1% (814/1888) were married. Of the 1888 patients, 57% (n=1076) presented with stage 1 disease, and the most common treatment modality was surgery alone (n=1068, 56.6%). The most common type of imaging performed during the initial 120- to 270-day window was chest computed tomography (1460/3278, 44.5%). Chest X-rays accounted for 36.3% (1190/3278) of all imaging performed, while the remaining 11.8% (386/3278) and 7.4% (242/3278) were positron emission tomography scans or other imaging modalities, respectively. Compared to the years 2008 to 2010, patients treated for NSCLC from 2014 to 2016 had a significantly higher likelihood of receiving guideline-concordant surveillance (hazard ratio 1.42, P<.001). Conclusions: In this unique application of a competing risks framework, the rate of guideline-concordant surveillance in this national cohort was lower than that reported in many previous studies. This finding highlights a potentially substantial gap in surveillance among eligible, asymptomatic lung cancer survivors. More strategies are needed to measure the true rate of guideline-concordant surveillance, along with education and advocacy to ensure guideline-concordant care.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.378
Teacher spread0.359 · 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 teacher head, not a consensus.

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 routes1
Has abstractyes

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