Planning for the Introduction of New CT Services in Rural and Remote Communities in Canada
Bibliographic record
Abstract
CT services are essential for diagnosing various conditions in Canada. There are existing challenges with access to these services due to the limited availability of machines, high demand for services, and long wait times. Rural and remote communities may experience additional challenges, as these services are often located in large urban centres. Canada’s Drug Agency (CDA-AMC) conducted an informal survey among senior medical imaging decision-makers to determine the factors that influence planning for new CT services in rural and remote communities in Canada. To better understand current access to CT services and support future planning, the average population served by CT services in rural and remote communities was also calculated. When establishing a population-based threshold for CT capacity in rural and remote areas, CDA-AMC found that, on average, a hospital with CT capabilities serves a population of approximately 56,100 people, with a catchment area of a 100 km radius. There are various factors that influence decision-making when planning for the placement of new CT services in rural and remote communities, some of which include the size of the community served, patient travel time to a primary care centre or travel distance to the nearest available CT facility, and the resources and costs associated with interfacility transfers and delayed scan times. The volume of exams and the clinical needs of hospitals are other important factors that influence CT planning in rural and remote communities. This demand and need for CT services informs decision-making around staffing and maintaining professional competency.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".