Remote multidisciplinary diagnostic discussion and spirometry for ILD patients in rural Virginia
Bibliographic record
Abstract
INTRODUCTION: Patients with interstitial lung disease (ILD) in rural areas face significant barriers to care and management of their disease. This study assessed the feasibility of remote ILD care using virtual multidisciplinary discussions (MDD) for accurate diagnosis and home-based spirometry for ILD management in rural Virginia. METHODS: A prospective feasibility study was conducted involving patients with suspected ILD living in rural Virginia. Participants were enrolled through their community pulmonary provider for discussion in the remote Virginia Commonwealth University (VCU) MDD. Following MDD recommendations were provided by the group and distributed back to their community physicians. These recommendations included additional diagnostic work-up as well as suggestions in adjustment in management. Additionally, subjects were also screened for home spirometry testing. The subjects were trained on the NuvoAir home spirometry system and followed for 6 months Results were then compared to in clinic spirometry at 3 and 6 months. RESULTS: Remote MDD discussions led to a change in diagnosis of 65% of subjects had a consensus diagnosis following MDD, including 47% of subjects with a change in preMDD diagnosis. For the 35% of subjects with determined “Unclassifiable ILD” referring physicians were provided additional diagnostic recommendations. Home spirometry was found to have excellent agreement with FVC measurement as compared to in clinic spirometry. Additionally, patient experience with home testing was strongly positive. CONCLUSION: The multimodal model of remote MDD and spirometric monitoring with home devices was determined to be acceptable and feasible for rural Virginian patients and their community pulmonary physicians. The benefit of this combination suggests an increase in diagnostic accuracy while providing support for rural providers on additional diagnostic testing and management for these patients as well as decreasing the patient burden of continued monitoring of these progressive diseases. However, additional exploration into the reasons for low referrals needs to be explored before this program could be further implemented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".