Quality Indicators and Benchmarks for Radiotherapy in Lung Cancer: A Modified Delphi Approach
Bibliographic record
Abstract
AIMS: While there are many published quality indicators (QIs) for assessing clinical care in lung cancer, few specifically measure the quality of radiotherapy (RT). To address this gap, we used a structured modified Delphi technique to develop a core set of QIs and benchmarks to evaluate RT processes for lung cancer treatment. MATERIALS AND METHODS: Candidate QIs identified from the systematic review were evaluated through survey consensus and deliberation by a multidisciplinary reference committee for inclusion in the initial survey and then after each round. A modified Delphi technique was employed across two rounds to reach consensus for QI development. The international expert survey panel consisting of radiation oncologists treating lung cancer rated QI importance, feasibility, and benchmarks, with consensus predefined as at least 70% of respondents reaching a threshold rating on a Likert scale. RESULTS: There were 70 respondents over two surveys, with 30 of the 47 QIs reaching the threshold for importance in the first Delphi round and 29 after the final Delphi round. Agreement ranged from 71% to 97% with 12 QIs reaching a consensus of 90% or more. Final consensus was reached as all 29 QIs were identified as feasible, and 27 of the suggested benchmarks were deemed acceptable. CONCLUSION: This core set of QIs provides a well-defined framework for evaluating RT processes in lung cancer treatment. They have the potential to establish a foundation for standardised quality measurement and benchmarking for guiding quality improvement efforts and improving patient outcomes.
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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 | 0.267 | 0.196 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| 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".