Implementation of Stereotactic Ablative Radiotherapy for the Treatment of Oligometastatic Cancer in Canada
Bibliographic record
Abstract
The aim of this Environmental Scan is to identify and describe the use of stereotactic ablative radiotherapy in Canada, the systems in place to manage the treatment of patients with oligometastatic cancer, and the barriers and facilitators to the implementation of this treatment. The findings are based on a literature review, 22 survey responses from stakeholders, and email- and video call-based follow-up consultations with select stakeholders. Ten Canadian jurisdictions were represented by the survey respondents, who were primarily radiation oncologists. Stereotactic ablative radiotherapy for the treatment of oligometastatic cancer is currently being accessed in all Canadian provinces as a standard treatment option. Centres are primarily treating oligometastases in the lungs, bones (non-spine), lymph nodes, spine, and liver. Some cancer care centres have the capacity for stereotactic ablative radiotherapy to treat localized primary tumours but do not treat oligometastatic sites. There is a variation in patient selection criteria and treatment guidelines across Canadian jurisdictions, with most facilities following institutional guidance for the processes required for patient prioritization and treatment. There is a lack of standardized consensus guidelines with common criteria. Reported facilitators for the implementation of stereotactic ablative radiotherapy for the treatment of oligometastatic cancer include access to dedicated equipment and teams. Reported barriers to its implementation include the lack of standardized patient selection and treatment guidelines, and constraints in equipment and staff resources (including time).
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".