Statistical primer: an introduction into the principles of Bayesian statistical analyses in clinical trials
Bibliographic record
Abstract
Trials in cardiac surgery are often hampered at the design level by small sample sizes and ethical considerations. The conventional analytical approach, combining frequentist statistics with null hypothesis significance testing, has known limitations and its associated P-values are often misinterpreted, leading to dichotomous conclusions of trial results. The Bayesian statistical framework may overcome these limitations through probabilistic reasoning and is subsequently introduced in this Primer. The Bayesian framework combines prior beliefs and currently obtained data (the likelihood), resulting in updated beliefs, also known as posterior distributions. These distributions subsequently facilitate probabilistic interpretations. Several previous cardiac surgery trials have been performed under a Bayesian framework and this Primer enhances the understanding of their basic concepts by linking results to graphical presentations. Furthermore, contemporary trials that were initially analysed under a frequentist framework, are re-analysed within a Bayesian framework to demonstrate several interpretative advantages.
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 | 0.054 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".