What is the optimal radiotherapy utilization rate for lung cancer?—a systematic review
Bibliographic record
Abstract
Lung cancer is a major cause of morbidity and mortality globally. Although radiotherapy (RT) may be beneficial in the radical and/or palliative management of many lung cancer patients, it is underutilized worldwide. Population-level development of RT resources requires estimates of optimal radiotherapy utilization rates (ORUR) and actual radiotherapy utilization rate (ARUR). A systematic review of PubMed database for English-language articles from January 2009 to January 2019 was performed. Keywords included utilization, underutilization, demand, epidemiologic, benchmark, RT and cancer. Data abstracted included: study population, diagnosis, stage, year of diagnosis, timing of RT, intent of RT, ARUR, and ORUR. Eligible studies provided ARUR or ORUR for lung cancer, small cell lung cancer (SCLC), or non-small cell lung cancer (NSCLC). Included ARUR were based on at least 1,000 patients who were diagnosed or treated in 2009 or later. Included ORUR were based on evidence review or ARUR in 2009 or later. The initial search strategy yielded 1,627 unique abstracts. After review, 105 articles were determined appropriate for full-text review. From these, a final set of 21 articles met all inclusion criteria. In eight papers, ORUR was estimated. Estimated lifetime ORUR ranged from 61% to 82%. Methods for estimation included the evidence-based guideline model, Malthus model, and criterion-based benchmarking (CBB) model. The majority of estimates (6/8) used the evidence-based guideline model. Fifteen papers provided ARUR on lung cancer, inclusive of SCLC and NSCLC. ARUR within 9 months to 1 year of diagnosis ranged from 39% to 46%. Lifetime ARUR was an estimated 52% in Ontario, Canada. Palliative intent ARUR ranged from 12% in Central Poland to 46% in Ontario, Canada. RT is underutilized for lung cancer globally, and there is wide geographical variation in the level of underutilization.
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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 | 0.015 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".