Why this work is in the frame
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Bibliographic record
Abstract
Problem definition: We study early-stage dose-finding clinical trials with simultaneous consideration of efficacy and toxicity without parametric assumptions on the forms of the unknown dose-efficacy and dose-toxicity curves. We propose algorithms that adaptively allocate doses based on patient responses, in order to maximize the efficacy for the patients during the trial while minimizing the toxicity. Methodology/results: We leverage online learning to design the clinical trial and propose two algorithms. The first one follows dose-escalation principles and analyzes the efficacy and toxicity simultaneously. The second one uses bisection search to identify a safe dose range and then applies upper confidence bound algorithms within the safe range to identify efficacious doses. We show the matching upper and lower bounds for the regret of both algorithms. We find that observing the dose-escalation principle is costly, as the optimal regret of the first algorithm is in the order of [Formula: see text], worse than the optimal regret of the second algorithm, which is in the order of [Formula: see text]. We test our proposed algorithms with three benchmarks commonly used in practice on synthetic and real data sets, and the results show that they are competitive with or significantly outperform the benchmarks. Managerial implications: We provide a novel insight that following the dose-escalation principle inevitably leads to higher regret. The first proposed algorithm is suitable to use when little information about the dose-toxicity profile is available, whereas the second one is appealing when more information is available about the toxicity profile. Funding: This work was supported by the National Science Foundation [Grant 1651912]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0246 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it