The Effect of Higher Protein Dosing in Critically Ill Patients: A Multicenter Registry‐Based Randomized Trial: The EFFORT Trial
Bibliographic record
Abstract
Current randomized trials and observational studies evaluating higher versus lower protein doses in critically ill patients yield inconclusive results. Because of few studies and methodologic limitations, clinical guidelines suggest a wide range of protein intake based on weak evidence. Clinical equipoise about protein dosing exists. The purpose of the current manuscript is to provide the rationale and protocol for a randomized controlled trial (RCT) of 4000 critically ill patients randomly allocated to receive a higher or lower protein dose. We propose a global, volunteer-driven, registry-based RCT involving >100 intensive care units (ICUs). We will enroll mechanically ventilated patients with high nutrition risk, identified by low (≤25) or high (≥35) body mass index, moderate to severe malnutrition, frailty, sarcopenia, or when >96-hour duration of mechanical ventilation is expected. Exclusion criteria include patients who are >96 hours since initiation of mechanical ventilation, moribund, or pregnant, and where the clinician lacks clinical equipoise regarding protein dose. The intervention consists of higher (≥2.2 g/kg/d) or lower (≤1.2 g/kg/d) protein dose, achieved by enteral nutrition, parenteral nutrition, or both. The primary outcome will be 60-day mortality. Key secondary outcomes include time-to-discharge alive from hospital, ICU and hospital survival, and length of stay. As this is research based on existing medical practice, we will apply for a waiver of informed consent, where possible. The large sample size is a reflection of the small signal we expect to see in this large, pragmatic trial.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".