Stereotactic Radiation therapy in early and locally advanced inoperable renal cell carcinoma: treatment outcomes, patterns of failure and risk factors
Bibliographic record
Abstract
Abstract Introduction: Renal cell carcinomas are the most common kidney neoplasms and though they are radioresistant, ablative radiation therapy has shown a good response rate in terms of tumor eradication and a high local control rate in both primary and metastatic stages. Apart from the various other interventions available, stereotactic body radiation therapy has shown significant progress in the management of both early and metastatic renal cell cancers. There is a plethora of literature, especially in terms of retrospective studies focusing on the clinical outcomes of the use of stereotactic body radiation therapy. In the systematic review, we aim to report the clinical outcomes and identify any high-risk factors for recurrences posttreatment in both early and metastatic renal cell cancer. Methodology: We aim to perform a systematic review and meta-analysis of the available data in the last 10 years and hematological regress in and transparent manner to identify patterns of failure and high-risk factors for recurrence. The protocol has been prepared following the preferred reporting items for systematic reviews and meta-analysis (PRISMA–P) 2015 guidelines and the protocol has been registered with the international prospective registry of systematic reviews (CRD42022380543).
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.015 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".