Bibliographic record
Abstract
Energy expenditure in critically ill children is a key determinant for nutritional caloric delivery in critically ill children. However, its methods of measurement are poorly available, and its prediction is unreliable and imprecise with currently available methodology. This thesis seeks to better understand patient and clinical factors related to energy expenditure to improve its prediction. In the first study, we conducted a systematic review to determine the patient and clinical factors associated with energy expenditure in critically ill patients. We described the limitations of currently available data and identified important factors that are not included in current prediction formulae of the critically ill. In the second study, we conducted a retrospective study at two Canadian centers to describe some predictors of energy expenditure in this population and estimate their magnitude of effect. We were able to describe some factors of interest in critically ill children, identify factors that were not evaluated in sufficient numbers, and determine new predictors associated with energy expenditure not previously described in paediatric ICU. In the third study, we aimed to establish a prospective cohort of critically ill children with indirect calorimetry measurements and clinical data to support the development and initial validation of an equation that predicts energy expenditure. This thesis achieved its intended aims and the work provides the foundation for the development of an energy expenditure equation that is reliable, valid, accessible, and widely applied in critically ill children.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".