The effect of high protein dosing in critically ill patients: an exploratory, secondary Bayesian analyses of the EFFORT Protein trial
Bibliographic record
Abstract
BACKGROUND: The EFFORT Protein trial assessed the effect of high vs usual dosing of protein in adult ICU patients with organ failure. This study provides a probabilistic interpretation and evaluates heterogeneity in treatment effects (HTE). METHODS: We analysed 60-day all-cause mortality and time to discharge alive from hospital using Bayesian models with weakly informative priors. HTE on mortality was assessed according to disease severity (Sequential Organ Failure Assessment [SOFA] score), acute kidney injury, and serum creatinine values at baseline. RESULTS: The absolute difference in mortality was 2.5% points (95% credible interval -6.9 to 12.4), with a 72% posterior probability of harm associated with high protein treatment. For time to discharge alive from hospital, the hazard ratio was 0.91 (95% credible interval 0.80 to 1.04) with a 92% probability of harm for the high-dose protein group compared with the usual-dose protein group. There were 97% and 95% probabilities of positive interactions between the high protein intervention and serum creatinine and SOFA score at randomisation, respectively. Specifically, there was a potentially relatively higher mortality of high protein doses with higher baseline serum creatinine or SOFA scores. CONCLUSIONS: We found moderate to high probabilities of harm with high protein doses compared with usual protein in ICU patients for the primary and secondary outcomes. We found suggestions of heterogeneity in treatment effects with worse outcomes in participants randomised to high protein doses with renal dysfunction or acute kidney injury and greater illness severity at baseline.
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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 | 0.047 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".