A Novel Lipid Emulsion Containing 18-Cn3 Fatty Acids Demonstrates Superior Liver Protection to SMOFlipid® in the Murine Model of Parenteral Nutrition
Bibliographic record
Abstract
Objectives: Individuals with obstructive sleep apnea condition need to control nutrient intake when receiving clinical care.Resting Energy Expenditure (REE) is estimated using predictive energy equations to guide the nutrient support in such cases.However current calculation methods only reach up to 45% of accuracy when compared with true REE values obtained by handheld indirect calorimetry devices.This study aims to develop an optimized model for better performance in REE prediction.Methods: Dataset was obtained from a single office of a private sleep medicine practice in Houston, Texas.The dataset contains data from 160 subjects with 13 attributes (height, weight, sex, age, etc.).Several regression machine learning models, namely Linear regression, Gradient Boosting Regression, Decision tree, Random Forest, K-Nearest Neighbors and Neural network models were developed and their predictions compared to REE measured by indirect calorimetry.Results: Among the models developed, Gradient Boosting Regression had the highest performance with 65.6% of predicted values falling within the band of acceptable agreement (10% of real value), followed by Random Forest (62.5%) and Linear regression as the lowest precision with only 37.5% of predictions within the band of acceptable agreement.Conclusions: We developed six machine learning models for the prediction of REE in patients with Sleep Apnea.The results show that Gradient Boosting Regression has better predictive accuracy than established REE equations.Further validation may provide support in confirming and optimizing this approach.
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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 | 0.000 |
| 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 | 0.000 | 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".