The effect of enteral nutrition strategy and non-invasive ventilation on diarrhea and nutritional goals in the critically ill: A protocol for a multicentre retrospective cohort study (ENND GOALS)
Bibliographic record
Abstract
Enteral nutrition (EN) has become the standard of care for nutritional support among critically ill patients. However, little is known about whether continuous delivery (CEN) or intermittent delivery (IEN) is preferable or the consequences of either strategy. This is particularly true for diarrhea, which is understudied but consistently shown to be associated with increased morbidity among critically ill patients. This is a multicenter, retrospective cohort study including critically ill patients greater than 18 years of age, admitted to the Intensive Care Unit in Hamilton, Canada, and prescribed EN for greater than 48 hours. Patients will be divided into IEN and CEN groups based on the nutritional strategy they received during their stay. The primary outcome will be the proportion of patients in each group with diarrhea during their ICU stay, diarrhea will be defined according to WHO criteria. Multivariate logistic regression will be performed to identify the role of covariates in the risk of developing diarrhea. Secondary outcomes will include caloric intake, incidence of ICU-acquired infections, including Clostridioides difficile, length of stay, and mortality. This study protocol has been approved by the Hamilton Integrated Research Ethics Board (#16453). The study findings will be disseminated at academic conferences and published in peer-reviewed journals.
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 | 0.040 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".