Multidisciplinary Videoconferencing for Physician Education and Remote Management of Interstitial Lung Disease
Bibliographic record
Abstract
Abstract Background The gold standard for interstitial lung disease (ILD) diagnosis is multidisciplinary discussion (MDD); however, access is often limited by geographic barriers, time constraints, and the number of centers with ILD expertise. Objective To assess the educational and clinical impact of a novel videoconferencing MDD program for the diagnosis and management of ILD. Methods We performed a retrospective observational study of the Multidisciplinary Interstitial Lung Disease Discussion with Experts Remotely (MILDDER) program, a videoconferencing MDD platform initiated by the Toronto General Hospital in Toronto, Canada. We used anonymized survey data from attendees (trainees and practicing physicians) and referring physicians who attended MILDDER between 2018 and 2023. Self-reported ILD confidence before and after MILDDER participation and general program satisfaction were assessed using a 10-point Likert scale. ILD confidence questions were stratified by clinical practice experience. Wilcoxon signed-rank testing for paired data was used to determine statistical significance in the subgroup that completed a MILDDER semester (bimonthly sessions for 6 mo). Written survey responses were assessed qualitatively and grouped by theme. Clinical outcomes, including patient characteristics, new or changed ILD diagnoses, new investigations requests, and new treatment suggestions, were assessed. Results Three hundred seventeen attendees and referring physicians completed pre-MILDDER questionnaires. Overall, they reported low confidence in their ability to diagnose and manage ILD. After they attended a MILDDER semester ILD, their confidence increased by a median of 3 to 4 points in the overall group. Among respondents with ≥5 years of clinical practice experience, there was no change in ILD diagnostic confidence after MILDDER; however, management confidence increased by a median of 2 points. A statistically significant increase in all areas of ILD confidence assessed was noted in the subgroup of 70 participants with complete pre- and post-MILDDER semester surveys. Respondents were generally very satisfied with MILDDER. New or changed ILD diagnoses occurred in 86 (50.6%) presented cases, new investigation requests occurred in 40 (22.7%) cases, and new medications were recommended for 30 (17%) cases. Conclusion Videoconferencing MDD platforms such as MILDDER are feasible and can be used as a tool for physician education and remote management of ILD.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".