Bibliographic record
Abstract
Abstract For a well-established class of medical products, when a similar or improved version of a product in the class has been developed, to evaluate the safety and effectiveness of the new product, the Bayesian approach is gaining popularity to save resource and reduce trial duration by leveraging highly relevant external data. However, the Bayesian approach can appear daunting to practitioners, including statisticians without sufficient exposure to the Bayesian methodology. Instead of using the typical modeling approach to borrow evidence from external data, this paper proposes to quantify the effective sample size from the external data first and then combine the effective portion of the external data with the current trial data through a Bayesian scheme. Guided by the fundamental principles of the central limit theorem and sufficient statistics, the proposed method provides a shortcut to assess and implement the Bayesian methodology in a transparent and generalized fashion. For two selected case studies, the proposed method generates comparable analysis results with those from the typical modeling approaches.
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.
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 | 0.032 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".