The population benefit of evidence-based radiotherapy: 5-Year local control and overall survival benefits
Bibliographic record
Abstract
BACKGROUND: To describe the population benefit of radiotherapy in a high-income setting if evidence-based guidelines were routinely followed. METHODS: Australian decision tree models were utilized. Radiotherapy alone (RT) benefit was defined as the absolute proportional benefit of radiotherapy compared with no treatment for radical indications, and of radiotherapy over surgery alone for adjuvant indications. Chemoradiotherapy (CRT) benefit was the absolute incremental benefit of concurrent chemoradiotherapy over RT. Five-year local control (LC) and overall survival (OS) benefits were measured. Citation databases were systematically queried for benefit data. Meta-analysis and sensitivity analysis were performed. FINDINGS: 48% of all cancer patients have indications for radiotherapy, 34% curative and 14% palliative. RT provides 5-year LC benefit in 10.4% of all cancer patients (95% Confidence Interval 9.3, 11.8) and 5-year OS benefit in 2.4% (2.1, 2.7). CRT provides 5-year LC benefit in an additional 0.6% of all cancer patients (0.5, 0.6), and 5-year OS benefit for an additional 0.3% (0.2, 0.4). RT benefit was greatest for head and neck (LC 32%, OS 16%), and cervix (LC 33%, OS 18%). CRT LC benefit was greatest for rectum (6%) and OS for cervix (3%) and brain (3%). Sensitivity analysis confirmed a robust model. INTERPRETATION: Radiotherapy provides significant 5-year LC and OS benefits as part of evidence-based cancer care. CRT provides modest additional benefits.
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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 | 0.026 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".