Evaluation of Acute Toxicity and Early Clinical Outcome in Head and Neck Cancers Treated With Conventional Radiotherapy and Simultaneous Integrated Boost Arc Radiotherapy
Bibliographic record
Abstract
Background: Chemoradiotherapy plays an important role in management of locally advanced head and neck cancers. This retrospective analysis was done to evaluate and compare acute toxicity profiles and early clinical outcomes in patients treated with conventional and arc techniques. Methods: Fifty-five patients of head and neck cancers were evaluated. Thirty patients received conventional radiotherapy with 6 MV or cobalt 60 and 25 patients were treated with simultaneous integrated boost-volumetric modulated arc radiotherapy (SIB-VMAT) with dose prescription of 66 - 70 Gy. Concurrent chemotherapy was given as cisplatin injection at 40 mg/m 2 weekly or 100 mg/m 2 thrice weekly. Results: The incidence of grade 3-4 mucositis was 56% versus 83.3% with SIB-VMAT and conventional treatments (P = 0.026). The incidence of grade 2-3 xerostomia was 44% versus 80% (P = 0.006) in the two groups. Grade 2 dysphagia was seen in 40% versus 80% (P = 0.008) favoring the arc treatments. Seventeen patients undergoing arc treatment had complete response compared to 14 in the conventional group (P = 0.040). The median disease-free survival (median ± standard error) was 16 months (11 ± 1.987 months) in the conventional and arc groups (P = 0.073). Conclusion: SIB-VMAT shows a better toxicity profile and a trend towards better disease-free survival when compared to conventional radiotherapy in head and neck cancers. World J Oncol. 2017;8(4):117-121 doi: https://doi.org/10.14740/wjon1049w Correction in World J Oncol. 2017;8(5):174-174, https://doi.org/10.14740/wjon1049wc1 Â
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".