Interstitial lung abnormalities management, a real-life survey of England Targeted Lung Health Check
Bibliographic record
Abstract
Interstitial lung abnormalities (ILAs) are incidental computed tomography (CT) findings affecting more than 5% of the lung parenchyma. Their management remains undefined, raising an emerging challenge in lung cancer screening programmes, such as the UK’s Targeted Lung Health Check (TLHC). This study aimed to assess current ILA management practices through an online survey. Data were collected via Qualtrics, and the survey was distributed via email, social media, and newsletters to healthcare professionals (HCPs) involved in diagnosing or managing interstitial lung disease (ILD) or TLHC referrals. A total of 87 participants completed the survey, including 65.8% respiratory physicians, 15.8% radiologists, 11.7% nurses, and 3.9% general practitioners. Among them, 80.8% worked in centres delivering TLHC. While 50.7% of respondents agreed or strongly agreed that ILAs are clinically important, 19.2% disagreed or strongly disagreed. Moreover, 20.5% strongly disagreed, and 47.9% disagreed that the distinction between ILA and ILD is clear. Amongst HCPs managing ILAs detected through TLHC (n=48), the majority (64.6%) did not follow a standardised management pathway, and 14.6% did not offer routine follow-up for ILA. When offered, follow-up was most commonly every 12 months (58.3%). The majority (70.9%) arranged periodic lung function monitoring; however, 43.8% reported that they did not offer routine CT follow-up. Duration of ILA follow-up was variable, with 35.4% following up for 3 years or less and 35.5% for five years or more. Conclusion: Despite being common incidental findings, ILAs lack a standardised management approach, leading to variability in patient care and services.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".