Assessing the efficacy of palliative radiation treatment schemes for locally advanced squamous cell carcinoma of the head and neck: a meta-analysis
Bibliographic record
Abstract
Background: The objective to assess the outcomes from different palliative radiotherapy (RT) schedules in incurable head and neck cancer (HNC), to evaluate if there is a relationship between RT dose, technique, and fractionation with tumor response in contrast to the occurrence of adverse effects. Materials and methods: Eligible studies were identified on Medline, Embase, the Cochrane Library, and annual meetings proceedings through June 2020. Following PRISMA and MOOSE guidelines, a cumulative meta-analysis of studies for overall response rate (ORR), overall survival (OS), progression-free survival (PFS), pain/dysphagia relief, and toxicity was performed. A meta-regression analysis was done to assess if there is a connection between RT dose, schedule, and technique with ORR. Results: Twenty-eight studies with 1,986 patients treated with palliative RT due to incurable HNC were included. The median OS was 6.5 months [95% confidence interval (CI): 5.6-7.4], and PFS was 3.6 months (95% CI: 2.7-4.3). The ORR, pain and dysphagia relief rates were 72% (95% CI: 0.6-0.8), 83% (95% CI: 52-100%), and 75% (95% CI: 52-100%), respectively. Conventional radiotherapy (2D-RT) or conformational radiotherapy (3D-RT) use were significantly associated with a higher acute toxicity rate (grade ≥ 3) than intensity-modulated radiation therapy (IMRT) or stereotactic body radiation therapy (SBRT). On meta-regression analyses, the total biological effective doses (BED) of RT (p = 0.001), BED > 60 Gy10 (p = 0.001), short course (p = 0.01) and SBRT (p = 0.02) were associated with a superior ORR. Conclusions: Palliative RT achieves tumor response and symptom relief in incurable HNC patients. Short course RT of BED > 60 Gy using IMRT could improve its therapeutic ratio. SBRT should be considered when available.
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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 | 0.024 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.075 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".