Is pre-trial quality assurance necessary? Experiences of the CONVERT Phase III randomized trial for good performance status patients with limited-stage small-cell lung cancer
Bibliographic record
Abstract
OBJECTIVE: This study is an analysis of the pre-trial quality assurance (QA) exercises submitted by clinicians from radiotherapy (RT) centres across Europe and Canada to qualify for participation in the CONVERT trial. METHODS: QA exercises submitted by 64 clinicians at 64 RT centres were included in this analysis. The exercises included the completion of a trial-specific questionnaire and submission of a treatment plan, for both trial arms, for a patient fitting the eligibility criteria of the trial. This article describes the QA programme set up for the CONVERT trial and identifies deviations from the trial protocol. Patient eligibility, disease and critical structure outlining and treatment planning technique were assessed. RESULTS: Results from QA trial-specific questionnaires received between February 2008 and September 2011, returned as part of the QA exercise, indicated that the majority of centres (70.3%) were using 6-MV photons and type B treatment planning system algorithms (57.8%). 90.6% of clinicians assessed submitted data for patients who fitted the eligibility criteria for the trial. There were inconsistencies in outlining of gross tumour volume (GTV) and organs at risk, mainly heart and oesophagus, and in the use of margins around the GTV. CONCLUSION: Such a QA programme helps to ensure that centres conform to trial protocol and should reduce inconsistencies in RT planning that may confound the results of the CONVERT trial. ADVANCES IN KNOWLEDGE: Few studies reporting pre-trial QA have been published to date. This article outlines the importance of such a QA programme in the context of multicentre Phase III studies.
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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 | 0.207 | 0.361 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".