Incidental pulmonary nodule management in Canada: exploring current state through a narrative literature review and expert interviews
Bibliographic record
Abstract
Background and Objective: Incidental pulmonary nodules (IPNs) are common and increasingly detected with the overall rise of radiologic imaging. Effective IPN management is necessary to ensure lung cancer is not missed. This study aims to describe the current landscape of IPN management in Canada, understand barriers to optimal IPN management, and identify opportunities for improvement. Methods: We performed a narrative literature review by searching biomedical electronic databases for relevant articles published between January 1, 2010, and November 22, 2023. To validate and complement the identified literature, we conducted structured interviews with multidisciplinary experts involved in the pathway of patients with IPNs across Canada. Interviews between December 2021 and May 2022 were audiovisual recorded, transcribed, and thematically analyzed. Key Content and Findings: A total of 1,299 records were identified, of which 37 studies were included for analysis. Most studies were conducted in Canada and the United States and highlighted variability in radiology reporting of IPNs and patient management, and limited adherence to recommended follow-up imaging. Twenty experts were interviewed, including radiologists, respirologists, thoracic surgeons, primary care physicians, medical oncologists, and an epidemiologist. Three themes emerged from the interviews, supported by the literature, including: variability in radiology reporting of IPNs, suboptimal communication, and variability in guideline adherence and patient management. Conclusions: Despite general awareness of guidelines, there is inconsistency and lack of standardization in the management of patients with IPNs in Canada. Multidisciplinary expert consensus is recommended to help overcome the communication and operational barriers to a safe and cost-effective approach to this common clinical issue.
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How this classification was reachedexpand
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 | 0.033 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".