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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it