Assessment of Successful Randomization Through a Machine Learning and Visualization Tool for Pre-Treatment Symptoms: Examples from CCTG/AGITG CO.17 and CO.20 Trials
Bibliographic record
Abstract
Patients in randomized controlled trials (RCTs) must be successfully randomized to reduce or eliminate bias. Because pre-treatment symptoms have prognostic significance in cancer patients, qualitative and quantitative tools were developed to assess similarity of baseline pre-treatment symptoms across different treatment arms of RCTs as one measure of randomization success. Clinician-reported symptom data from two colorectal cancer RCTs, CO.20 and CO.17, were used to demonstrate the utility of a qualitative visualization tool and quantitative machine learning K-means tool, which grouped patients into clusters using baseline symptoms. Qualitatively, reflection bar graphs (RBGs) visualized potential imbalances in baseline symptoms (i) across treatment arms and (ii) by corresponding patient clusters identified within each treatment arm. RBGs found that the treatment arms for both RCTs had similar symptom profiles, while the lack of significant differences in the proportions of patients in each cluster across treatment arms further confirmed successful randomization. This paper details the creation of visualization, machine-learning, and statistical tools to compare baseline symptoms across RCT treatment arms, demonstrating that the CO.20 and CO.17 trials were successfully randomized by baseline symptoms and are comparable. These tools can therefore be implemented easily to ensure an extra layer of quality assurance of the randomization process for study assessment.
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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 | 0.209 | 0.496 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".