The interstitial lung disease patient pathway: from referral to diagnosis
Bibliographic record
Abstract
Background: Suspected interstitial lung disease (ILD) patients may be referred to an ILD-specialist centre or a non-ILD-specialist centre for diagnosis and treatment. Early referral and management of patients at ILD-specialist centres has been shown to improve survival and reduce hospitalisations. The COVID-19 pandemic has affected the ILD patient diagnostic pathway and prompted centres to adapt. This study investigates and contrasts ILD patient pathways in ILD-specialist and non-ILD-specialist centres, focusing on referrals, caseloads, diagnostic tools, multi-disciplinary team (MDT) meeting practices and resource accessibility. Methods: Conducted as a cross-sectional study, a global self-selecting survey ran from September 2022 to January 2023. Participants included ILD specialists and healthcare professionals (HCPs) from ILD-specialist centres and non-ILD-specialist centres. Results: Of 363 unique respondents from 64 countries, 259 were from ILD-specialist centres and 104 from non-ILD-specialist centres. ILD centres had better resource availability, exhibiting higher utilisation of diagnostic tests (median: 12 tests) than non-ILD centres (nine tests) and better access to specialist professions attending MDT meetings (median: six professions at meeting) in specialist centres than non-ILD centres (three professions at meeting). Transitioning to virtual MDT meetings allowed HCPs from other locations to join meetings in nearly 90% of all centres, increasing regular participation in 60% of specialist centres and 72% of non-ILD centres. For treatment of patients, specialist centres had better access to antifibrotic drugs (91%) compared to non-ILD centres (60%). Conclusions: Diagnostic pathways for ILD patients diverged between specialist centres and non-ILD centres. Disparities in resource and specialist availability existed between centres.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".