Relationships between family physicians’ referral for palliative radiotherapy, knowledge of indications for radiotherapy, and prior training: a survey of rural and urban family physicians
Bibliographic record
Abstract
BACKGROUND: The primary objective of this research was to assess the relationship between FPs' knowledge of palliative radiotherapy (RT) and referral for palliative RT. METHODS: 1001 surveys were sent to FPs who work in urban, suburban, and rural practices. Respondents were tested on their knowledge of palliative radiotherapy effectiveness and asked to report their self-assessed knowledge. RESULTS: The response rate was 33%. FPs mean score testing their knowledge of palliative radiotherapy effectiveness was 68% (SD = 26%). The majority of FPs correctly identified that painful bone metastases (91%), airway obstruction (77%), painful local disease (85%), brain metastases (76%) and spinal cord compression (79%) can be effectively treated with RT, though few were aware that hemoptysis (42%) and hematuria (31%) can be effectively treated. There was a linear relationship between increasing involvement in palliative care and both self-assessed (p < 0.001) and tested (p = 0.02) knowledge. FPs had higher mean knowledge scores if they received post-MD training in palliative care (12% higher; p < 0.001) or radiotherapy (15% higher; p = 0.002). There was a strong relationship between FPs referral for palliative radiotherapy and both self-assessed knowledge (p < 0.001) and tested knowledge (p = 0.01). CONCLUSIONS: Self-assessed and tested knowledge of palliative RT is positively associated with referral for palliative RT. Since palliative RT is underutilized, further research is needed to assess whether family physician educational interventions improve palliative RT referrals. The current study suggests that studies could target family physicians already in practice, with educational interventions focusing on hemostatic and other less commonly known indications for palliative RT.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".