Additional file 1 of Simple compared to covariate-constrained randomization methods in balancing baseline characteristics: a case study of randomly allocating 72 hemodialysis centers in a cluster trial
Bibliographic record
Abstract
Additional file 1: Appendix 1. Common data sources used for population-based studies. Appendix 2. Complete list of 156 Baseline characteristics for the randomization and trial population cohorts. Appendix 3. Randomization of the 72 clusters using PROC PLAN in SAS. Appendix 4. a Prognostic baseline characteristics that were thought to be relevant a priori or correlated with the outcome from previous literature. b Baseline characteristics from the Population for Randomization that were subjected to principal component analysis. Appendix 5. Algorithm for capturing primary composite outcome. Appendix 6. Results from Principal component analysis (PCA). Appendix 7. We used the principal axis method to extract the principal components. A varimax (orthogonal) rotation followed the principal axis method. Only the first ten components displayed eigenvalues greater than 1 (see Appendix 6), and the results of a scree test also suggested that only the first ten components were meaningful. Therefore, we retained the first ten components for rotation. Appendix 8. Hardware specification and optimization for running the constrained randomization process. Appendix 9. The percentage of times baseline characteristics were balanced across 1000 randomization schemes for the three techniques.
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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 | 0.014 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.867 | 0.068 |
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".