Estimating the benefit and cost of radiotherapy for lung cancer
Bibliographic record
Abstract
PURPOSE: To estimate the benefit and cost of using radiotherapy (RT) in the initial management of lung cancer in the general population. METHODS: We identified indications for RT in the initial management of small cell and non-small cell lung cancer through a review of the literature. The proportion of patients with each specific indication for treatment was determined using epidemiological observations from cancer registry data and from the literature. We estimated the benefit gained from RT use for each indication in the model using values published in the literature. We estimated the cost of RT for each indication using published Canadian data. The total benefit and cost was calculated for all indications combined. Results are reported in 2001 Canadian dollars. RESULTS: The mean benefit was 7 months of survival for each lung cancer patient treated with curative intent and 3 months of symptom control for each patient treated with palliative intent. The average cost was 9881 dollars per life year gained and 13,938 dollars per year of symptom control gained. Sensitivity analysis revealed values between 7905 dollars and 19,762 dollars per year of survival gain and between 10,368 dollars and 27,875 dollars per year of symptom control gained. CONCLUSIONS: Using RT in the initial management of lung cancer can provide considerable gains in survival and symptom control. The cost of RT for the initial management of lung cancer is inexpensive compared with a common cut off of 50,000 dollars per life year gained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".