Clinical Outcomes Related to Protein Delivery in a Critically Ill Population
Bibliographic record
Abstract
OBJECTIVE: Optimal intake of energy and protein is associated with improved outcomes, although outcomes relative to protein intake are very limited. Our purpose was to evaluate the impact of prescribed protein delivery on mortality and time to discharge alive (TDA) using data from the International Nutrition Survey 2013. We hypothesized that greater protein delivery would be associated with lower mortality and shorter TDA. METHODS: The sample included patients in the intensive care unit (ICU) ≥ 4 days (n = 2828) and a subsample in the ICU ≥ 12 days (n = 1584). Models were adjusted for evaluable nutrition days, age, body mass index, sex, admission type, acuity scores, and geographic region. Percentages of prescribed protein and energy intake were compared with mortality outcomes using logistic regression and with Cox proportional hazards for TDA. RESULTS: Mean intake for the 4-day sample was protein 51 g (60.5% of prescribed) and 1100 kcal (64.1% of prescribed); for the 12-day sample, mean intake was protein 57 g (66.7% of prescribed) and 1200 kcal (70.7% of prescribed). Achieving ≥ 80% of prescribed protein intake was associated with reduced mortality (4-day sample: odds ratio [OR], 0.68; 95% confidence interval [CI], 0.50-0.91; 12-day sample: OR, 0.60; 95% CI, 0.39-0.93), but ≥ 80% of prescribed energy intake was not. TDA was shorter with ≥ 80% prescribed protein (hazard ratio [HR], 1.25; 95% CI, 1.04-1.49) in the 12-day sample but longer with ≥ 80% prescribed energy in the 4-day sample (HR, 0.82; 95% CI, 0.69-0.96). CONCLUSION: Achieving at least 80% of prescribed protein intake may be important to survival and shorter TDA in ICU patients. Efforts to achieve prescribed protein intake should be maximized.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".