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 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 | 0.008 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".