Navigating the Complexity of Lung Cancer Surveillance Practices: Qualitative Pilot Study on Provider Perspectives
Bibliographic record
Abstract
Background: Surveillance is noted to be an important part of survivorship to detect recurrence and/or second primary lung cancer (SPLC) at a curable stage. However, current surveillance guidelines remain controversial, and the factors providers consider in clinical decision-making are neither well-defined nor consistently applied. Objective: In order to inform the qualitative protocol for a larger national study, this pilot study aimed to understand the factors that influence lung cancer surveillance and how providers view risk stratification as a potential tool to inform surveillance practices. Methods: Semistructured interviews were conducted between October 2023 and July 2024 with purposively sampled providers involved in treating and surveilling patients with lung cancer from the US-based Palo Alto Veterans Affairs Medical Center and Stanford Medicine and its affiliate clinics. Providers were recruited through both email outreach and in-person invitations. Interviews were transcribed by an external transcription service and analyzed through a qualitative inductive content analysis approach to identify themes. Results: In total, 11 physicians and 2 advanced practice providers (N=13) participated in interviews. The majority were from medical specialties (n=8, 61.5%), and the average number of years of practice as a provider was 9 years. A total of 3 themes were identified that describe the clinicians' sentiments about current surveillance practices and how a risk stratification tool could be used in screening for recurrence and/or SPLC. Clinicians consider a variety of clinical and nonclinical factors (category 1: factors that influence clinical decision making) and highlighted limits of a risk stratification tool, including concerns about generalizability, accuracy, and validity (category 2: sentiments toward a hypothetical risk stratification tool). Finally, concerns were raised about how delivering risk stratification data might impact patient anxiety, misinterpretation, and adherence to surveillance plans (category 3: delivery of risk stratification data to patients). Conclusions: This qualitative analysis highlights the complexity of lung cancer surveillance decision-making and provider concerns about tool accuracy and delivery. While risk stratification tools may support surveillance decisions, their further development must address data quality, accuracy across diverse clinical and nonclinical risk factors, and effective patient-level data delivery. Doing so will facilitate the practical implementation of risk stratification tools to improve surveillance of SPLC and recurrence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".