Integrating Interstitial Lung Abnormality Detection Into Lung Cancer Screening: A Mixed Methods Approach for Care Pathway Development
Bibliographic record
Abstract
Abstract Rationale: Interstitial lung abnormalities (ILAs) are incidental parenchymal findings detected on computed tomography scans (CTs). There is a 7% prevalence of ILAs in lung cancer screening (LCS) cohorts, and their presence is associated with increased all-cause mortality. ILAs may progress to clinical fibrotic interstitial lung disease (ILD), however heterogeneity exists therefore there is a need to risk stratify these patients to ensure standardized follow up and efficient use of clinical resources. This study examined current respirology and primary care physician's (PCP) practice patterns and explored the need to develop a care pathway for ILAs detected during LCS. Methods: This mixed-methods study included surveys and structured interviews with respirologists and PCPs in Alberta, Canada. Online surveys were distributed via email invitation to all licensed respirologists within the province, as well as primary care networks, and participants volunteered for one-on-one interviews. Interviews were conducted using the Theoretical Domains Framework and Consolidated Framework for Implementation Research. Survey results are presented with descriptive frequencies. Qualitative data were transcribed, coded and analyzed according to grounded theory principle. Results: Respirologists: A response rate of 33% (n=39/117) was achieved (Table 1). Respirologists reported feeling comfortable managing ILA, but most (64%) did not follow a standardized approach. Pulmonary function tests (PFTs) were recommended by 67% of respondents, and 36% recommended a high-resolution CT. The majority (69%) believed ILAs should be followed for at least 3 years, while 15% only followed patients if ILD was present (symptoms, abnormal PFTs). Key themes from 11 interviews included knowledge, risk stratification, and suggestions for care pathway standardization. PCP: 17 participated in the online survey and 7 participated in interviews. The majority (59%) were not familiar with the term ILA and 100% of respondents thought there should be a standardized care pathway (Table 1). Key interview themes included radiology report guidance, personal referral practices and resource limitations. Conclusions: Respirologists frequently manage patients with ILAs, but their approaches vary. Most patients are followed longitudinally, which requires healthcare resources, including consultation, lung function and imaging. PCP were unfamiliar with the term ILA, and would value a standardized care pathway for this patient population. With the establishment of LCS programs, a large number of patients with ILAs will be detected. Our findings highlight the need for targeted health care resources to manage this patient population and proposes respirologist and PCP-informed recommendations for implementing a standardized care pathway.
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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 | 0.136 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".