International patterns of palliative care in neuro-oncology: a survey of physician members of the Asian Society for Neuro-Oncology, the European Association of Neuro-Oncology, and the Society for Neuro-Oncology
Bibliographic record
Abstract
Abstract Background Brain tumor patients have limited survival and suffer from high morbidity requiring specific symptom management. Specialized palliative care (PC) services have been developed to address these symptoms and provide end-of-life treatment. Global utilization patterns of PC in neuro-oncology are unknown. Methods In a collaborative effort between the Society for Neuro-Oncology (SNO), the European Association of Neuro-Oncology (EANO), and the Asian Society for Neuro-Oncology (ASNO), a 22-question survey was distributed. Wilcoxon 2-sample and Kruskal-Wallis tests were used to assess differences in responses. Results Five hundred fifty-two evaluable responses were received. The most significant differences were found between Asia-Oceania (AO) and Europe as well as AO and United States/Canada (USA-C). USA-C providers had more subspecialty training in neuro-oncology, but most providers had received no or minimal training in palliative care independent of region. Providers in all 3 regions reported referring patients at the onset of symptoms requiring palliation, but USA-C and European responders refer a larger total proportion of patients to PC (P < .001). Physicians in AO and Europe (both 46%) as well as 29% of USA-C providers did not feel comfortable dealing with end-of-life issues. Most USA-C patients (63%) are referred to hospice compared with only 8% and 19% in AO and Europe (P < .001), respectively. Conclusion This is the first report describing global differences of PC utilization in neuro-oncology. Significant differences in provider training, culture, access, and utilization were mainly found between AO and USA-C or AO and Europe. PC patterns are more similar in Europe and USA-C.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".