Spine Metastasis Practice Patterns among Korean, Chinese, and Japanese Radiation Oncologists: A Multinational Online Survey Study
Bibliographic record
Abstract
This online survey of practising radiation oncologists from Korea, China and Japan was conducted to investigate the current practices in radiotherapy (RT) for spine metastasis and to compare these practices across the three countries. The questionnaire included nine general information questions and two clinical scenarios (representing 'typical' and 'good' prognosis spine metastasis), with seven questions for each scenario. An anonymous web-based survey using Google Docs® was undertaken from 2 September 2014 to 9 April 2015. A total of 54 Korean, 107 Chinese and 104 Japanese radiation oncologists participated in the study. The first scenario involved a typical case of spine metastasis (~25% expected 1-year survival rate), and the preferred fractionation scheme was 10 fractions of 3 Gy, though the pattern was slightly different in each country. The second scenario involved a good prognosis case (>50% expected 1-year survival rate), and 10 fractions of 3 Gy was the preferred practice in all three countries (however, use of a larger fraction dose with a smaller fraction number was more common in Korea). A more conformal RT technique was more prominent in China and Korea, especially for patients with a good prognosis. Avoidance of reirradiation was notable in China. In summary, a preference for multiple fractionation in RT for spine metastasis was observed in the majority of Korean, Chinese and Japanese radiation oncologists, although there were slight differences in practice preferences, especially for patients with a favorable prognosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".