Health Services Virtual Organization (HSVO) NEP11 Final Report
Bibliographic record
Abstract
The goal of the Health Services Virtual Organization (HSVO) project was to create a sustainable research platform for experimental development of shared ICT-based health services. This was based around the development of a focused collection of functioning services supporting patient treatment planning and team & individual preparedness in the operating room, emergency room, general practice clinics, and patients’ bedsides. A virtual organization was created to develop the platform. The partners were Lakehead University in Thunder Bay, the Northern Ontario School of Medicine in Sudbury and Thunder Bay, the Communications Research Centre and iDeal Consulting in Ottawa, McGill University in Montréal (the Centre for Intelligent Machines and the McGill Simulation Centre), the National Research Council in Fredericton and Innovations in Learning in California. Non-funded partners were added later including the University of Wisconsin-La Crosse and University College Cork (UCC) in Ireland. The platform was based on using Inocybe’s Argia (www.inocybe.ca; software formerly known as UCLP); and NRC’s SAVOIR; Service-oriented Architecture for a Virtual Organization's Infrastructure and Resources (formerly known as Eucalyptus). SAVOIR would connect and control multiple ‘edge devices’ at multiple locations across an articulated private network. The planned edge devices were: • OpenLabyrinth: this is an open source narrative game engine used for creating and running virtual patient cases. • Laerdal SimMan 3G: this is a commercial medical simulation mannequin platform consisting of a human model and a range of software tools for creating and executing training scenarios. • Remote Stereo Viewer (RSV) is a tool for visualizing 3D image datasets, the case of HSVO sourced from the Bassett Collection running from a server at CENIC in California. • VOLSEG is a tool for visualizing volumetric datasets, in the case of the HSVO project a volumetric visual and interactive rendering of the visible human dataset. • Camera array is a means to produce virtual camera views either live or from a stored dataset by capturing multiple video camera feeds observing the same scene from a grid perspective. • General web resources such as the Canadian Medical Association’s Clinical Practice Guidelines. The high level objectives were to: • Establish an articulated high speed and high capacity articulated private network (APN) between all of the project partners and then establish lightpath capabilities over this network. • Set up a number of existing edge devices and develop others (in particular a camera array) • Set up SAVOIR as the middleware hub and connect the edge devices through it via the APN so that they can be controlled from SAVOIR at multiple locations • Conduct trials and evaluations of the platform with medical learners
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.098 | 0.035 |
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".