Referral Practices of Oncologists to Specialized Palliative Care
Bibliographic record
Abstract
PURPOSE: To describe current referral practices of oncologists to specialized palliative care (SPC) and define demographic characteristics, practice situations, and opinions associated with referral. METHODS: Physician members of the Canadian Association of Medical Oncologists, Canadian Association of Radiation Oncologists, and Canadian Society of Surgical Oncology were invited to participate in an anonymous survey assessing SPC referral practices. Participants received two e-mailed and two mailed invitations. RESULTS: The response rate was 72% (603 of 839 physicians); 37% were medical oncologists/hematologists, 50% were radiation oncologists, and 12% were surgical oncologists. Ninety-four percent reported that SPC was available to them, but only 37% reported that these services accepted patients on chemotherapy. Eighty-four percent referred terminally ill patients usually/always, but generally for uncontrolled symptoms or discharge planning late in the disease course. One third would refer to SPC earlier if it was renamed supportive care. Predictors of higher referral frequency included comprehensiveness of available SPC services (P = .004), satisfaction with SPC availability (P < .001), SPC acceptance of patients receiving chemotherapy (P < .001), and oncologist ease with referring patients to a palliative care service before they were close to death (P < .001). Controlling for specialty, predictors of referral at diagnosis or during chemotherapy, rather than later, included satisfaction with SPC service availability (P < .001) and SPC service acceptance of patients on chemotherapy (P < .001). CONCLUSION: Oncologists referred patients frequently to SPC, but generally late in the disease course for patients with uncontrolled symptoms. Availability of comprehensive SPC, especially for patients receiving chemotherapy, and persisting definitional issues seem to be the main barriers preventing timely 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".