Local Surgical, Ablative, and Radiation Treatment of Metastases
Bibliographic record
Abstract
Because local therapies directed toward a specific tumor mass are known to be effective for treating early-stage cancers, it should be no surprise that there has been considerable historical experience using local therapies for metastatic disease. In more recent years, increasing interest in the use of local therapy for metastases likely has arisen from improvements in systemic therapy. In the absence of effective systemic therapies, such local treatments were often considered futile given both the difficulty in eliminating all sites of identifiable metastatic disease as well as realities regarding the rapid natural history of uncontrolled tumor dissemination. However, with a higher likelihood of patients surviving longer after effective systemic therapy, even if not cured, the goal of the eradication of residual metastases via potent local therapies can be rationalized. However, this rationalization should be evidence-based so as to avoid harming patients for no established benefit. Although surgical metastectomy remains the most common and first-line standard among local therapies, nonsurgical alternatives, including thermal ablation and stereotactic body radiotherapy, have become increasingly popular because they are generally less invasive than surgery and have demonstrated considerable promise in eradicating macroscopic tumor. Rather than eliminating the need for local therapies, improvements in systemic therapies appear to be increasing the prudent utilization of modern local therapies in patients presenting with more advanced cancer.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".