Bibliographic record
Abstract
Virtual remote imaging services have the potential to help alleviate some of Canada’s capacity challenges in medical imaging. However, in some circumstances, this is contingent on expanding access to imaging services in rural, remote, and underserved locations. These services are being piloted at a site in British Columbia for CT imaging, and there are also plans to implement virtual remote imaging services at a site in Manitoba for CT imaging and later expanding its use to mobile MRI. Virtual remote imaging services can help increase workforce capacity by enabling medical radiation technologists (MRTs) to remotely assist with imaging examinations. This technology has the potential to improve access to specialized expertise, optimize staffing, and offer flexible coverage during peak demand or staff shortages. Training and professional development opportunities can be broadened using virtual remote imaging services by enabling MRTs to learn from experienced professionals in different locations, thereby promoting skill growth and enhancing their expertise. The successful deployment of virtual remote imaging services requires effective communications between onsite and remote staff, standardizing staffing policies, establishing standards and quality assurance protocols, navigating licensing complexities, ensuring data security, and integrating technology systems to enable high-quality image transmission.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.117 | 0.024 |
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".