Statistical inference for the randomized play the winner design
Bibliographic record
Abstract
In clinical tlials rvith extleme outcomes, it is ethically desilable to treat as many patients as possible rvith the superior tleatment.Adaptive designs accomplish this ivhile still producing statistically meaningful results.One such design is knorvn as the landomized play the rvinner lule (RPWR).Trvo asymptotic methods fol consttucting confidence intervals based on data from the RPWR ale plesented and compaled by simulation.It is found that both methods pelform ri'ell fol small sample sizes despite being approximate methods.Some othel aspects of the RPWR are examined, sucli as the rate of convelgence of a martingale central limit theorem and sorne appealing ploperties of the allocation probabilities.lVIy sincerest glatitude and appreciation nust go to my supervisol Dr. Xikui \4/ang.He first introduced
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".