Randomized Trials in Critical Care Nutrition
Bibliographic record
Abstract
BACKGROUND: The purpose of this methodological review is to quantify and qualify critical care nutrition randomized controlled trials (RCTs) that inform our practice, to evaluate their strengths and limitations, and to recommend strategies for improving the design of future trials in this area. METHODS: The literature was systematically reviewed to find all RCTs published between 1980 and December 2008 that evaluated nutrition interventions in critical care. Data were abstracted on the nature and quality of included RCTs. RESULTS: A total of 207 RCTs met the inclusion criteria. Of these, 170 (82.1%) were single-center, and 37 (17.9%) were multicenter. The largest number of trials evaluated intensive insulin therapy (n = 25), arginine-supplemented diets (n = 22), and supplemental parenteral glutamine (n = 17). The first RCTs were published in 1983 (n = 2), and the mean sample size was 39.0. In 2008, there were 26 RCTs, each enrolling an average of 237.1 patients. Excluding 2 cluster RCTs, 62 of 205 (30.2%) trials had concealed randomization, 125 of 205 (61.0%) reported on intention-to-treat analyses, and 69 of 205 (33.7%) had a double-blinded intervention; 18 of 205 (8.8%) studies reported on all 3 design characteristics. Currently, 60 critical care nutrition RCTs (18 multicenter trials) are registered on clinical trials registries. CONCLUSIONS: The future of clinical critical care nutrition research is promising, with more trials of increasing sample size being conducted. Robust trial methodology, transparent reporting, and the development of research networks will help to further advance this important field.
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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 | 0.321 | 0.607 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".