Protocol for an observational cohort study integrating real-world data and microsimulation to assess imaging surveillance strategies in stage I–IIIA NSCLC patients in OneFlorida+
Bibliographic record
Abstract
INTRODUCTION: Although lung cancer remains the leading cause of cancer deaths in the US, recent advances in early detection and treatment have led to improvements in survival. However, there is a considerable risk of recurrence or second primary lung cancer (SPLC) following curative-intent treatment in patients with early-stage non-small cell lung cancer (NSCLC). Professional societies recommend routine surveillance with CT to optimise the detection of potential recurrence and SPLC at a localised stage. However, no definitive evidence demonstrates the effect of imaging surveillance on survival in patients with NSCLC. To close these research gaps, the Advancing Precision Lung Cancer Surveillance and Outcomes in Diverse Populations (PLuS2) study will leverage real-world electronic health records (EHRs) data to evaluate surveillance outcomes among patients with and without guideline-adherent surveillance. The overarching goal of the PLuS2 study is to assess the long-term effectiveness of surveillance strategies in real-world settings. METHODS AND ANALYSIS: PLuS2 is an observational study designed to assemble a cohort of patients with incident pathologically confirmed stage I/II/IIIA NSCLC who have completed curative-intent therapy. Patients undergoing imaging surveillance will be followed from 2012 to 2026 by linking EHRs with tumour registry data in the OneFlorida+ Clinical Research Consortium. Data will be consolidated into a unified repository to achieve three primary aims: (1) Examine the utilisation and determinants of CT imaging surveillance by race/ethnicity and socioeconomic status, (2) Compare clinical endpoints, including recurrence, SPLCs and survival of patients who undergo semiannual versus annual CT imaging and (3) Use the observational data in conjunction with validated microsimulation models to simulate imaging surveillance outcomes within the US population. To our knowledge, this study represents the first attempt to integrate real-world data and microsimulation models to assess the long-term impact and effectiveness of imaging surveillance strategies. ETHICS AND DISSEMINATION: This study involves human participants and was approved by the University of Florida Institutional Review Board (IRB), University of Florida IRB 01, under approval number IRB202300782. The results will be disseminated through publications and presentations at national and international conferences. Safety considerations encompass ensuring the confidentiality of patient information. All disseminated data will be de-identified and summarised.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| grok | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| opus | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.051 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.049 | 0.014 |
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, unvalidatedLabeled directly by 3 models reading the full record.
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".