The Impact of Multidisciplinary Discussion (MDD) in the Diagnosis and Management of Fibrotic Interstitial Lung Diseases
Bibliographic record
Abstract
Accurate diagnosis of interstitial lung disease (ILD) is crucial for management and prognosis but can be challenging even for experienced clinicians. Expert multidisciplinary discussion (MDD) is considered the reference standard for ILD diagnosis; however, there remain concerns regarding lack of validation studies and relative limited information on the impact of MDD in real-life clinical practice. The goal of this study was to assess the effect of MDD in providing a specific ILD diagnosis, changing the diagnosis provided upon referral, and to determine how often and in which way MDD altered management. Material and Methods . Retrospective observational study in an ILD referral tertiary academic center. MDD diagnoses were categorized as specific, provisional, and unclassifiable ILD. Pre-MDD and MDD diagnoses were compared for change in diagnosis and concordance rates for specific diagnoses. Relevant change in management including initiation or change in pharmacological treatment, referral to surgical biopsy, and nonpharmacological management were recorded. Results . 126 cases were included (79M, 47F, 36–93 years, mean 70 y). Specific MDD diagnosis was provided in 62% (78/126); 12% (15/126) had provisional diagnosis, and 21% (27/126) was unclassifiable. Overall agreement for specific pre-MDD and MDD diagnosis was 41% (52/126) and 80% for idiopathic pulmonary fibrosis (IPF) diagnosis. MDD altered diagnosis in 37% (47/126) and changed management in 39% (50/126). Amongst concordant diagnoses, management was altered in 46% (24/52). In summary, MDD provided a specific diagnosis discordant with pre-MDD diagnosis in a significant proportion of cases and was particularly valuable in the diagnosis of non-IPF ILD. MDD often altered management and had relevant impact on management even in cases with concordant pre-MDD diagnosis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".