Radiation Oncologists’ Perspectives on Oligometastatic Prostate Cancer: A Survey from Korean Oligometastasis Working Group
Bibliographic record
Abstract
BACKGROUND: Interest in the oligometastatic prostate cancer (OMPC) is increasing, and various clinical studies have reported the benefits of metastasis-directed radiation therapy (MDRT) in OMPC. However, the recognition regarding the adopted definitions, methodologies of assessment, and therapeutic approaches is diverse among radiation oncologists. This study aims to evaluate the level of agreement for issues in OMPC among radiation oncologists. METHODS: We generated 15 key questions (KQs) for OMPC relevant to definition, diagnosis, local therapies, and endpoints. Additionally, three clinical scenarios representing synchronous metastatic prostate cancer (mPC) (case 1), metachronous mPC with visceral metastasis (case 2), and metachronous mPC with castration-resistance and history of polymetastasis (case 3) were developed. The 15 KQs were adapted according to each scenario and transformed into 23 questions with 6-9 per scenario. The survey was distributed to 80 radiation oncologists throughout the Republic of Korea. Answer options with 0.0-29.9%, 30-49.9%, 50-69.9%, 70-79.9%, 80-89.9%, and 90-100% agreements were considered as no, minimal, weak, moderate, strong, and near perfect agreement, respectively. RESULTS: = 2) agreements were shown in nine. For the case recognized as OMPC with agreements of 93% (case 1), near perfect agreements on the application of definitive radiation therapy (RT) for whole metastatic lesions were achieved. While ≥70% agreements regarding optimal dose-fractionation for metastasis-directed RT (MDRT) has not been achieved, stereotactic body RT (SBRT) is favored by clinicians with higher clinical volume. CONCLUSION: For the case recognized as OMPC, near perfect agreement for the application of definitive RT for whole metastatic lesions was reached. SBRT was more favored as a MDRT by clinicians with a higher clinical volume.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".