0-7-21 hypofractionated palliative radiotherapy: an effective treatment for advanced head and neck cancers
Bibliographic record
Abstract
OBJECTIVE: We report our experience in providing palliative radiotherapy (RT) to patients with head and neck cancers (HNCs). Our hypofractionated regimen, "0-7-21", treats patients with 24 Gy in three fractions. METHODS: Patients, disease and response data were retrieved for candidates of 0-7-21 from 2005 to 2012. Primary end points included symptom and tumour size responses to RT based on response evaluation criteria in solid tumours (RECIST) guidelines. Secondary end points included progression-free survival (PFS) within the irradiated field, overall survival (OS) and symptomatic PFS (SPFS), calculated using Kaplan-Meier method and adverse events. Cox proportional hazards regression and logistic regression were used to investigate for prognostic factors. RESULTS: A total of 110 patients were included. Among the patients, 40% and 31% had complete response for symptoms and tumour size, respectively; 42% and 50% had partial response for symptoms and tumour size, respectively; and 15% had stability of symptoms and tumour size. Median 6-month OS was 51%, and PFS within the irradiated field was 39%. Planning target volume was predictive of OS (p < 0.001), PFS (p < 0.001) and SPFS (p < 0.005), while higher TNM stage was associated with poorer tumour response (p = 0.02). CONCLUSION: 0-7-21 is an effective and well-tolerated palliative RT regimen for patients with HNC. There was excellent symptom and local control with acceptable toxicity profile in these patients. ADVANCES IN KNOWLEDGE: This is the first study to describe the outcomes of 0-7-21 in treating advanced HNCs. The positive results suggest that 0-7-21 provides excellent palliation with minimal toxicity, with significantly less on-treatment time than current published palliative RT regimen.
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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 | 0.000 |
| 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".