Stereotactic Radiation Therapy for De Novo Head and Neck Cancers: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Purpose Stereotactic body radiation therapy (SBRT) for de novo (previously untreated) head and neck cancers (HNCs) is increasingly being used in medically unfit patients. A systematic review of SBRT was conducted for previously untreated HNCs. Methods and Materials Medline (PubMed), excerpta medica database, and Cochrane Library databases were queried from inception until July 2020. Comparative outcome data were extracted where available up to 5 years. Results from random-effect models were presented in forest plots, with between-study heterogeneity evaluated by I 2 statistics and Q-tests. Results Nine studies met inclusion criteria, representing 157 patients. Local control rates at 1, 2, and 3 years were as follows: 90.7% (95% confidence interval, 80.6%-95.6%), 81.8% (67.2%-90.7%), and 73.5% (40.4%-90.5%), respectively. Overall survival at 1, 2, and 3 years was 75.9% (75.1%-76.6%), 61.1% (60.3%-61.9%), and 50.0% (48.8%-51.4%), respectively. Late grade 3 to 4 toxicity rate was 3.3% (0.2%-10.2%), and late grade 5 toxicity rate was 0.1% (0.0%-1.0%). Conclusions SBRT for de novo HNC is safe and effective in providing locoregional control, with acceptable toxicities in most subsites. This finding warrants broader validation to guide its scope.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".