Association between protein dose in the early and late acute phases of critical illness and time‐to‐discharge‐alive: A secondary analysis of a randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Research has sought to identify optimal protein doses during acute phases of critical illness to optimize outcomes. METHODS: A secondary analysis of the EFFORT Protein trial, which compared high vs usual protein (N = 1301). Only participants with 8 evaluable days of protein intake were included in our analysis. Mean protein intake was categorized as low (<0.8), medium (0.8-1.3), or high (>1.3 g/kg/day). Acute illness phases were define as early (days 1-4) and late (days 5-8). Participants were grouped by protein dose received in each phase. Based on prior evidence, early phase medium protein/late phase high protein served as the referent. The primary outcome was time-to-discharge-alive; secondary outcomes included 60-day mortality and discharge home. RESULTS: We identified 819 participants (median [IQR] age 59.0 [46.0, 69.0] years; 60% male). Time-to-discharge-alive did not differ significantly across groups (P = 0.19). The early low/late high-protein and early high/late high-protein groups had hazard ratios of 0.63 (95% CI, 0.35-1.11) and 0.70 (95% CI, 0.46-1.06), respectively. Mortality and discharge-home rates did not differ significantly across protein dose/acute phase groups (P = 0.85 and 0.65, respectively). CONCLUSION: We hypothesized that early medium and late high protein would improve outcomes; however, no significant differences between were observed across protein dose/acute phase groups. These findings are hypothesis-generating and highlight the need for future research to identify biomarkers or scoring tools that better define phase transitions in critical illness, enabling more precise nutrition strategies. TRIAL REGISTRATION (PRIMARY): NCT03160547.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".