Cumulative Equivalence: Controlling for Inter-Individual Differences at Baseline Characteristic Testing of RCTs
Bibliographic record
Abstract
Randomized control trials (RCTs) are regarded as the gold standard for intervention research. The randomization process is intended to establish comparability between groups, so that the study outcomes can be attributable to the intervention, rather than group differences. The purpose of this paper is to emphasize the inherent risks of conducting multiple tests in the establishment of equivalency at baseline while omitting the cumulative effect of small group differences in RCTs. Randomization does not thoroughly prevent differences in group averages at the specific characteristic level. Any baseline differences that benefit the intervention group when accumulated over multiple categories of demographic characteristics described herein as cumulative inequivalence can significantly impact the internal validity of RCTs. This paper describes a procedure for assessing for cumulative inequivalence, as well as procedures such as re-randomization prior to intervention to establish comparability and thus promote cumulative equivalence of RCTs.
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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 | 0.370 | 0.807 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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".