Stereotactic body radiation therapy (SBRT) in the management of non-small-cell lung cancer: Clinical impact and patient perspectives
Bibliographic record
Abstract
Stereotactic body radiation therapy (SBRT) has emerged as a new technology in radiotherapy delivery, allowing for potentially curative treatment in many patients previously felt not to be candidates for radical surgical resection of stage I non-small-cell lung cancer (NSCLC). Several studies have demonstrated very high local control rates using SBRT, and more recent data have suggested overall survival may approach that of surgery in operable patients. However, SBRT is not without unique toxicities, and the balance of toxicity, and effect on patient-reported quality of life need to be considered with respect to oncologic outcomes. We therefore aim to review SBRT in the context of important patient-related factors, including quality of life in several domains (and in comparison to other therapies such as conventional radiation, surgery, or no treatment). We will also describe scenarios in which SBRT may be reasonably offered (i.e. elderly patients and those with severe COPD), and where it may need to be approached with some caution due to increased risks of toxicity (i.e. tumor location, patients with interstitial lung disease). In total, we hope to characterize the physical, emotional, and functional consequences of SBRT, in relation to other management strategies, in order to aid the clinician in deciding whether SBRT is the optimal treatment choice for each patient with early stage NSCLC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".