HHT Virtual Imaging Case Conferences - A New Way Forward for Rare Disease Management
Bibliographic record
Abstract
Introduction: Interdisciplinary case conferences are known to improve patient care in multisystem diseases. Medical imaging in key affected organ systems is integral to effective HHT management. The health jurisdiction of Alberta, Canada has a province-wide mandate for medical imaging storage developed through federal and provincial grants, which has been a tremendous resource to develop case management discussions virtually. This connectivity of imaging provides opportunities to discuss cases from across many areas of the province. We aimed to develop a platform for HHT multi-disciplinary medical imaging case conferences across Alberta. Methods: Pilot work for HHT imaging case conferences was carried out in Edmonton (2003-2013). A structured approach to medical imaging case conferences was reviewed and optimized to ensure participation by rural areas, respecting appropriate data sharing, privacy legislation and patient consent. The province-wide interdisciplinary framework was deployed in 2014. Results: Province-wide HHT interdisciplinary imaging case conferences were initially deployed through AHS, and later via a secure on-line platform during the COVID pandemic. Team members include Respirologists, Interventional Radiologists, Neurologists, Geneticists, Hematologists, nurses, trainees and researchers with appropriate patient permission (4 sessions/yr, 2-3 cases/session). Conclusion: Deployment of province-wide HHT imaging case conferences allows participation by many clinicians, including those from rural areas of the province. Future directions include pursuing inter-provincial / territorial case conferences to facilitate broader outreach of diagnostic and therapeutic expertise in HHT medicine.
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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 | 0.028 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".