P065 <break /> Virtual multidisciplinary discussion: Feasibility and diagnostic concordance with face-to-face multidisciplinary discussion
Bibliographic record
Abstract
Background: Multidisciplinary discussion (MDD) is considered the gold standard for ILD diagnosis, however, centers where MDD is performed are limited. A virtual MDD (vMDD) meeting provides an attractive alternative to center-based MDD. Aim: To describe a vMDD model and determine if vMDD is feasible and achieves similar diagnoses to MDD. Methods: Cases with established MDD diagnoses were randomly selected from a longitudinal database. Chart review was performed using a standardized form to extract clinical data, and HRCT scans and surgical lung biopsy images were digitized. A vMDD meeting was held involving two pulmonologists, one radiologist and one pathologist, all with expertise in ILD. Data were viewed synchronously so as to mimic a face-to-face MDD. The vMDD diagnosis and diagnostic level of confidence was recorded and compared to the face-to-face MDD diagnosis. Results: 21 cases were reviewed. Diagnostic agreement between vMDD and MDD was 61.9% (13/21). Concordant diagnoses were IPF (8), hypersensitivity pneumonitis (3), and unclassifiable (2). Discordant diagnosis occurred in 8 cases (TABLE); the majority were rated as low or medium confidence diagnoses by the expert panel. Diagnostic agreement for IPF was good (kappa = 0.71).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".