Clinical Outcomes in Critically Ill Patients Associated With the Use of Complex vs Weight‐Only Predictive Energy Equations
Bibliographic record
Abstract
BACKGROUND: The energy intake goal is important to achieving energy intake in critically ill patients, yet clinical outcomes associated with energy goals have not been reported. METHODS: This secondary analysis used the Improving Nutrition Practices in the Critically III International Nutrition Surveys database from 2007-2009 to evaluate whether mortality or time to discharge alive is related to use of complex energy prediction equations vs weight only. The sample size was 5672 patients in the intensive care unit (ICU) ≥ 4 days and a subset of 3356 in the ICU ≥ 12 days. Mortality and time to discharge alive were compared between groups by regression, controlling for age, sex, admission type, Acute Physiology and Chronic Health Evaluation II score, ICU geographic region, actual energy intake, and obesity. RESULTS: There was no difference in mortality between the use of complex and weight-only equations (odds ratio [OR], 0.90; 95% confidence interval [CI], 0.86-1.15), but obesity (OR, 0.83; 95% CI, 0.71-0.96) and higher energy intake (OR, 0.65; 95% CI, 0.56-0.76) had lower odds of mortality. Time to discharge alive was shorter in patients fed using weight-only equations (hazard ratio [HR], 1.11; 95% CI, 1.01-1.23) in patients staying ≥ 4 days and with greater energy intake (HR, 1.19; 95% CI, 1.06-1.34) in patients in the ICU ≥ 12 days. CONCLUSION: These data suggest that higher energy intake is important to survival and time to discharge alive. However, the analysis was limited by actual energy intake <70% of goal. Delivery of full goal intake will be needed to determine the relationship between the method of determining energy goal and clinical outcomes.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".