Design of nutrition trials in critically ill patients: food for thought
Bibliographic record
Abstract
We would like to thank Drs. Casaer and Van den Berghe for their thoughtful editorial on our article "Permissive underfeeding or standard enteral feeding in critically ill adults" published in the New England Journal of Medicine on June 18, 2015 (1,2).Over the last few years, several large clinical trials have added immensely to our knowledge regarding nutritional support of critically ill patients.Table 1 summarizes and contrasts eight recent multicenter trials which compared different doses of enteral nutrition (1,3-5), or enteral versus parenteral nutrition (6-9).PermiT and other trials showed no difference in outcomes in patients receiving restricted versus full caloric intake.Drs.Casaer and Van den Berghe raise many important questions regarding these trials: Is mortality an appropriate primary endpoint for nutrition trials?Do we need larger trials to detect smaller treatment effect?Should we use different endpoints than mortality?How about biomarkers?How generalizable are the results of PermiT to normal weight or underweight patient populations?Are specific patient groups more likely to be nutrition-responsive?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.228 | 0.409 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".