Trial Analysis and Interpretation in Critical Care Using the Evidential (Likelihood) Approach: Rationale and Practical Considerations
Bibliographic record
Abstract
Abstract Selecting the optimal methodological framework for evidence synthesis presents a fundamental challenge in contemporary clinical research. In critical care, in which many interventions yield inconclusive results under traditional P value–based analyses, complementary analytical approaches can enhance our understanding of trial data. Although frequentist statistics remain predominant and Bayesian methods have recently experienced a resurgence of interest, the evidential (or likelihood) framework offers a methodological perspective that potentially bridges these two inferential paradigms. In this Concise Translational Review, we introduce readers to the evidential approach. To present the evidential approach as an analytical tool for critical care trials, we demonstrate its application using data from two mechanical ventilation trials (ART [the Alveolar Recruitment Trial], N = 1,010; and STAMINA [Strategy for Community Acquired Pneumonia Trial], N = 214) and one trial evaluating balanced solutions (BaSICS [Balanced Solutions in Intensive Care Study], N = 10,520). We focus on how concepts and terminology translate across paradigms, the framework’s measures of effect (i.e., likelihood ratios, support values, and support intervals), proposals for its use in sequential analysis and trial monitoring, and how to report results from this framework in research articles. We propose that the evidential framework provides a clinically intuitive approach to trial interpretation by focusing on the relative evidence between competing hypotheses, thereby offering additional and complementary insights that align with clinical reasoning processes. To facilitate implementation by the scientific community, we have developed an interactive Shiny (open-source web-based) application (https://fzampier.shinyapps.io/Likelihood_Shiny/).
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 | 0.459 | 0.535 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.040 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".