Optimal Team for Diagnostic Imaging Equipment Installation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As jurisdictions expand imaging capacity to meet rising demand, incorporating diverse perspectives from those involved in the equipment’s use and installation can optimize health care resource value. Information on the composition of the team involved in consultations on the replacement or installation of new imaging equipment is not well reported in the literature. An informal survey provided a snapshot of the professionals involved in this process across Canada. While there are some differences in the professionals engaged in consultations on the replacement or installation of new imaging equipment many similarities exist across the country. The most commonly engaged professionals include diagnostic imaging (DI) technologists, managers and directors of imaging departments, medical physicists and procurement staff. Other commonly consulted professionals include radiologists, project managers, electrical planners, architects, mechanical planners and picture and archive communication system (PACS) coordinators. Gaining insight into the diverse professional perspectives involved in the planning, design, construction, installation and use of equipment may enhance the optimization of imaging services, by identifying valuable perspectives that may drive effective implementation and operation.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it