Guidelines for nutritional support in intensive care unit patients: a critical analysis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Guidelines are supposed to be helpful in clinical practice. Guidelines are also supposed to rest upon the evidence that there is. In the field of clinical nutrition the problem is that many clinical trials are not conclusive because they are underpowered and sometimes have an inferior design. RECENT FINDINGS: The publication of the Canadian guidelines one year ago initiated a lively debate. The Canadian guidelines used meta-analysis as a tool to review the literature. This resulted in both a sound evaluation of studies as well as some controversial recommendations. The Canadian guidelines are here put in a perspective in which the older type of guidelines are compared, and some of the points of recommendation are scrutinized. SUMMARY: What all guidelines agree upon is the shortage of solid knowledge, the conviction that complications related to nutritional therapy in the intensive care unit are not acceptable, and that enteral nutrition is preferable if it can be given without risk. Beyond that, many controversies remain and the need for high quality prospective studies must be emphasized. In addition, such studies must address the clinically important questions that the guidelines try to answer.
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 | 0.030 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".