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 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 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".