Optimal N-of-1 Clinical Trials for Individualized Patient Care and Aggregated N-of-1 Designs
Why this work is in the frame
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Bibliographic record
Abstract
Precision medicine typically refers to the use of genomic signatures of patients to assign more effective therapies to treat patients, or, for improved diagnosis of the early onset of a disease so that interventions can be delivered to prevent or delay the disease progression. Because the aim is to provide individualized patient treatment, such single-person trials are called N-of-1 trials. This chapter reviews fundamental ideas, models, and construction of optimal designs for N-of-1 trials, which are invariably constructed from crossover trials, where each patient receives a random sequence of trial treatments over time. We construct examples of universally optimal N-of-1 designs for comparing two treatments under various correlation structure assumptions and discuss how N-of-1 trials may be combined to form optimal aggregated N-of-1 trials for assessing average treatment effects for two or more treatments.
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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 | 0.019 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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