Has Radiotherapy Been Successfully Implemented in Alberta’s Small Cities? A Review of Alberta’s Regional Cancer Centre Network from 2010–2020
Bibliographic record
Abstract
The expansion of cancer services closer to home has become a major focus of publicly funded healthcare, with cancer organizations attempting to invest in smaller centers by integrating radiotherapy into these facilities. In Canada this has resulted in Ontario, British Columbia and Alberta investing in 12 expanded regional centers over the past 20 years. Quebec, Manitoba and Nova Scotia have made similar investments. Alberta's three new centers opened in 2010, 2013 and 2021 (projected). This study examined improvements in wait times and patient throughput between 2010 and 2020, and highlighted strategies that will support the sustainability and growth of clinical activity through to 2030. Significant improvement in ready to treat wait times for radiotherapy have resulted from opening two centers, and the provincial throughput for patients requiring systemic or radiotherapy has gone up by 16%. A patient satisfaction survey demonstrated that rural patients are happy with their care and desire the provision of more of their cancer treatment closer to home. An expert panel provided recommendations on what needs to be done to stabilize recruitment and retention.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".