Relationship Between Nutrition Intake and Outcome After Subarachnoid Hemorrhage: Results From the International Nutritional Survey
Bibliographic record
Abstract
BACKGROUND: A previous study suggested an association between low caloric intake(CI), negative nitrogen balance, and poor outcome after subarachnoid hemorrhage(SAH). Objective of this multinational, multicenter study was to investigate whether clinical outcomes vary by protein intake(PI) or CI in SAH patients adjusting for the nutritional risk as judged by the modified NUTrition Risk in the Critically Ill (mNUTRIC) score. METHODS: The International Nutrition Survey(INS) 2007-2014 was utilized to describe the characteristics, outcomes and nutrition use. A subgroup of patients from 2013 and 2014(when NUTRIC score was captured) examined the association between CI and PI and time to discharge alive(TTDA) from hospital using Cox regression models, adjusting for nutrition risk classified by the mNUTRIC score as low(0-4) or high(5-9). RESULTS: and APACHE-2 score 19.4 ± 7.0. Majority(85%) received enteral nutrition(EN) only, with a time to initiation of EN of 35.4 ± 35.2 hours. 64% had EN interrupted. Patients received a CI of 14.6 ± 7.1 calories/kg/day and PI 0.7 ± 0.3 grams/kg/day corresponding to 59% and 55% of total prescribed CI and PI respectively. In the 2013 and 2014 subgroup there were 226 SAH patients with a mNUTRIC score of 3.4 ± 1.8. Increased CI and PI were associated with faster TTDA among high mNUTRIC patients(HR per 20% of prescription received = 1.34[95% CI,1.03 -1.76] for CI and 1.44[1.07 -1.93] for PI), but not low mNUTRIC patients(CI: HR = 0.95[0.77 -1.16] PI:0.95[0.78 -1.16]). CONCLUSIONS: Results from this multicenter study found that SAH patients received under 60% of their prescribed CI and PI. Further, achieving greater CI and PI in hi risk SAH patients was associated with improved TTDA. mNUTRIC serves to identify SAH patients that benefit most from artificial nutrition and efforts to optimize protein and caloric delivery in this subpopulation should be maximized.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".