Penn State equation versus indirect calorimetry for nutritional assessment in patients with traumatic brain injury
Bibliographic record
Abstract
Background: Nutritional assessment can be challenging in patients with traumatic brain injury (TBI), and indirect calorimetry may be a more suitable method than predictive equations. We compared the Penn State equation versus the gold standard of indirect calorimetry for the nutritional assessment of patients with TBI, and quantified the difference between nutritional requirements and actual patient intake. Methods: This single-centre, prospective cohort study included patients with moderate (Glasgow Coma Scale score 9–12) and severe (Glasgow Coma Scale score 3–8) TBI admitted to the Montreal General Hospital intensive care unit (ICU) between June 2018 and March 2019. Penn State equation estimates and indirect calorimetry measurements were collected, and actual intake was drawn from medical records. We compared the 2 assessment methods using a Spearman correlation coefficient. Results: Twenty-three patients with TBI (moderate in 7 and severe in 16) were included in the study. Overall, there was a moderate positive correlation between the Penn State equation estimate and indirect calorimetry readings (correlation coefficient 0.457, p = 0.03); however, the correlation was weaker in severe TBI (correlation coefficient 0.174, p = 0.5) than in moderate TBI (correlation coefficient 0.929, p = 0.003). When compared to indirect calorimetry assessment, patients received 5.4% (p = 0.5) of required intake on the first day and 43.9% (p = 0.8) of required daily intake throughout their ICU stay. Conclusion: Patients with moderate or severe TBI in the ICU received less than 50% of their nutritional requirements. The difference between the Penn State equation and indirect calorimetry assessments was most noticeable for patients with severe TBI, which indicates that indirect calorimetry may be a more suitable tool for assessment of nutritional needs in this population.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".