P18 Implementation of regional interstitial lung disease multidisciplinary meetings between tertiary and secondary care
Bibliographic record
Abstract
Introduction Interstitial lung diseases (ILD) are a group of progressive conditions with an unpredictable trajectory, requiring both early referral and intervention from specialist care. Diagnosis is often complex and challenging, with many patients being initially misdiagnosed, despite increased awareness. Multidisciplinary (MDT) discussion in previous studies has demonstrated a new or altered pre-existing diagnosis or change in clinical management. Subsequently, effective management of ILD requires a collaborative approach between secondary care and ILD specialist centres. Aims and Objectives To explore how specialist ILD input via regional MDT can improve patient care locally and facilitate earlier appropriate referral to tertiary care. We also hope to improve ILD management with a collaborative approach between secondary care and ILD specialist centres, in line with NHS England directives. Methods From 2023 to mid-2025, regional MDT meetings were organised between the tertiary service and all the major referring trusts. The timing, duration and frequency of the MDT were decided by secondary care, in accordance with local needs. Attendees included an ILD Consultant from tertiary care, local respiratory physicians, radiology and, where possible, rheumatology and respiratory nurses. Subsequent to successful implementation in 2023/4, 4 criteria were developed and used as part of a clinical audit for 8 referring trusts to identify outcomes for each case discussion. Outcome data was measured from January-June 2025. Results Please see table 1. Conclusion Regional MDM work had a positive impact on care delivery by facilitating the following: Reducing potentially unnecessary referrals to tertiary care (CAT 1 & 2) Limiting travel and cost expenses for patients. Reduced waiting times for specialist centre (CAT 3 & 4) - routine referral waiting time reduced by 8 weeks, urgent referrals now seen 1–2 weeks. Increasing ILD education for consultants and trainees with better pre-referral diagnostic work-up. More patients required a single visit only to tertiary care. Improved interprofessional collaboration with rheumatology and allied healthcare professionals, strengthening multidisciplinary working. Earlier and safe discharge from tertiary service to secondary care. This model of care effectively aligns with the future requirements of NHS England to provide high-quality care for ILD closer to home.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".