International survey of the treatment of metastatic spinal cord compression.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Treatment of metastatic spinal cord compression (MSCC) varies significantly. It is useful to understand how radiation oncologists worldwide deal with these challenging and urgent cases. Therefore, a survey of practice patterns of metastatic spinal cord compression was performed among the members of the major radiation oncology organizations in the world to help improve clinical practice. MATERIAL AND METHODS: The survey questions addressed common clinical issues related to the diagnosis and treatment of spinal cord compression in the context of the available data. The survey of practice pattern in the management of MSCC was performed in 2010. There were a total of 269 survey respondents, and 90% of respondents were from hospital-based practice. Statistical analyses were performed at ASTRO headquarter using Microsoft Excel and SPSS. RESULTS: The practice pattern of initial diagnostic and clinical evaluation of patients for MSCC was fairly uniform across the continents and countries. Treatment decision was largely based on patient's general condition, overall oncologic status, and concomitant systemic chemotherapy in this survey. EBRT dose and fractionation patterns were determined by considering the estimated survival time, neurological status such as ambulatory status, previous radiation, and radiation treatment volume. Despite of using similar factors in making treatment decision, there was a significant difference in selecting the radiation dose and fractionation scheme. Selection of re-treatment radiation dose also varied and generally below the published tolerance dose. CONCLUSIONS: Selection of radiation dose and fractionation varied significantly among different continents and countries, while using similar factors to make treatment decision.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".