Palliative Thoracic Radiotherapy for Lung Cancer: A Systematic Review
Bibliographic record
Abstract
PURPOSE: The optimal dose of radiotherapy (RT) to palliate symptomatic advanced lung cancer is unclear. We systematically reviewed randomized controlled trials (RCTs) of palliative thoracic RT. METHODS: RCTs comparing two or more dose fractionation schedules were reviewed using the random-effects model of a freely available information management system. The relative risk and 95% CI for each outcome were presented in Forrest plots. Exploratory analysis comparing dose schedules after conversion to the time-adjusted biologically equivalent dose (BED) was performed to investigate for a dose-response relationship. RESULTS: A total of 13 RCTs involving 3,473 randomly assigned patients were identified. Outcomes included symptom palliation, overall survival, toxicity, and reirradiation rate. For symptom control in assessable patients, lower-dose (LD) RT was comparable with higher-dose (HD), except for the total symptom score (TSS): 65.4% of LD and 77.1% of HD patients had improved TSS (P = .003). Greater likelihood of symptom improvement was seen with schedules of 35 Gy(10) versus lower BED. At 1 year after HD and LD RT, 26.5% versus 21.7% of patients were alive, respectively (P = .002). Sensitivity analysis suggests this survival improvement was seen with 35 Gy(10) BED schedules compared with LDs. Physician-assessed dysphagia was significantly greater in the HD arm (20.5% v 14.9%; P = .01), and the likelihood of reirradiation was 1.2-fold higher after LD RT. CONCLUSION: No significant differences were observed for specific symptom-control end points, although improvement in survival favored HD RT. Consideration of palliative thoracic RT of at least 35 Gy(10) BED may therefore be warranted, but must be weighed against increased toxicity and greater time investment.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".