Critical care nutrition: a Bayesian re-analysis of trial data
Bibliographic record
Abstract
PURPOSE OF REVIEW: Nutritional support and optimal glucose control are key components of care during critical illness, yet evidence surrounding their use remains conflicting, making translation into clinical practice challenging. This review explores Bayesian methods to enhance the interpretation of frequentist critical care trials, particularly for interventions with inconclusive outcomes. RECENT FINDINGS: Bayesian re-analysis frameworks may clarify conflicting evidence in critical care, thus enhancing interpretability and supporting clinical decision-making. This review focuses on the Bayesian re-analysis of three recent trials with indeterminate results for mortality - NUTRIREA-3, EFFORT Protein, and TGC-Fast - that examined the effects of enteral nutrition and glucose control strategies. SUMMARY: We re-analyzed the mortality outcomes of these trials within a Bayesian framework, contrasting our findings with the original trial results to illustrate how Bayesian methods can enhance the clinical applicability of trial outcomes. Although Bayesian and frequentist analyses generally agree on the direction and magnitude of effect, Bayesian methods offer the advantage of providing posterior probabilities of benefit and harm, thus identifying promising and potentially harmful interventions. This review underscores the value of Bayesian analysis in re-evaluating clinical trial data and guiding clinical practice.
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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 | 0.162 | 0.452 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".