The characterisation of interstitial lung disease multidisciplinary team meetings: a global study
Bibliographic record
Abstract
Multidisciplinary team (MDT) diagnosis of interstitial lung disease (ILD) has been proposed as a gold standard, but there are no formal recommendations for MDT process or composition and limited knowledge regarding prevalence in routine practice. We performed a systematic evaluation of ILD diagnostic practice across a range of healthcare settings around the world. Electronic questionnaires were distributed across all global regionsviasociety and collaborators networks. Responses from 457 unique centres across 64 countries were included in the analysis. Of the 350 (76.6%) centres holding formal meetings, the majority held face-to-face MDT meetings (80%), for a minimum of 30 min (93%), and discussed diagnosis (96.9%) and patient management (94.9%) at the meetings. Compared with non-academic and academic non-ILD centres, ILD academic centres reported a higher ILD caseload, held more formal MDT meetings, and were more likely to include histopathology and rheumatology specialists in their diagnostic team. Of the centres holding MDT meetings, 5.5% routinely discussed all new cases at such meetings. An MDT approach to ILD diagnosis is consistently interpreted and widely implemented across a range of routine care settings around the world. This observation will inform future ILD diagnostic agreement studies and diagnostic pathway recommendations.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".