Barriers to palliative radiotherapy referral: A Canadian perspective
Bibliographic record
Abstract
Radiotherapy is an effective but underutilized treatment modality for cancer patients. We decided to investigate the factors influencing radiotherapy referral among family physicians in our region. A 30-item survey was developed to determine palliative radiotherapy knowledge and factors influencing referral. It was sent to 400 physicians in eastern Ontario (Canada) and the completed surveys were evaluated. The overall response rate was 50% with almost all physicians seeing cancer patients recently (97%) and the majority (80%) providing palliative care. Approximately 56% had referred patients for radiotherapy previously and 59% were aware of the regional community oncology program. Factors influencing radiotherapy referral included the following: waiting times for radiotherapy consultation and treatment, uncertainty about the benefits of radiotherapy, patient age, and perceived patient inconvenience. Physicians who referred patients for radiotherapy were more than likely to provide palliative care, work outside of urban centres, have hospital privileges and had sought advice from a radiation oncologist in the past. A variety of factors influence the referral of cancer patients for radiotherapy by family physicians and addressing issues such as long waiting times, lack of palliative radiotherapy knowledge and awareness of Cancer Centre services could increase the rate of appropriate radiotherapy patient referral.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".