Definitive Stereotactic Body Radiotherapy (SBRT) for Extracranial Oligometastases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Stereotactic body radiotherapy (SBRT) is often used to treat patients with oligometastases (OM). Yet, patterns of SBRT practice for OM are unknown. Therefore, we surveyed radiation oncologists internationally, to understand how and when SBRT is used for OM. METHODS: A 25-question survey was distributed to radiation oncologists. Respondents using SBRT for OM were asked how long they have been treating OM, number of patients treated, organs treated, primary reason for use, doses used, and future intentions. Respondents not using SBRT for OM were asked reasons why SBRT was not used and intentions for future adoption. Data were analyzed anonymously. RESULTS: We received 1007 surveys from 43 countries. Eighty-three percent began using SBRT after 2005 and greater than one third after 2010. Eighty-four percent cited perceived treatment response/durability as the primary reason for using SBRT in OM patients. Commonly treated organs were lung (90%), liver (75%), and spine (70%). SBRT dose/fractionation schemes varied widely. Most would offer a second course to new OM. Nearly all (99%) planned to continue and 66% planned to increase SBRT for OM. Of those not using SBRT, 59% plan to start soon. The most common reason for not using SBRT was lack of clinical efficacy (48%) or lack of necessary image guidance equipment (34%). CONCLUSIONS: Radiation oncologists are increasingly using SBRT for OM. The main reason for not using SBRT for OM is a perceived lack of evidence demonstrating clinical advantages. These data strengthen the need for robust prospective clinical trials (ongoing and in development) to demonstrate clinical efficacy given the widespread adoption of SBRT for OM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it