Accuracy of resting energy expenditure predictive equations in coronavirus disease 2019 (COVID-19) survivors
Bibliographic record
Abstract
Background & Aims Coronavirus disease 2019 (COVID-19) may be associated with abnormal energy metabolism and lead to inaccurate resting energy expenditure (REE) estimations by predictive equations. Here, we report measured REE (mREE) of a group of COVID-19 survivors and compared its accuracy against predicted REE (pREE). Methods This was a cross-sectional analysis of patients who survived COVID-19 prior to July 2021. An indirect calorimeter was used for mREE and compared against 21 pREE equations, 10 of which used a measure of body composition. Paired t-tests and Bland-Altman analysis were used to evaluate agreement and relative accuracy or bias for percentage error between pREE and mREE; measurements within ±10% were considered accurate. Results We assessed 38 COVID-19 survivors; age: 48.5y (interquartile range: 40.2, 60.0), body mass index: 29.3±5.6 kg/m 2 , mREE: 1520± 275 kcal/d, time since COVID-19: 183.2 ±34.4 days. Ten (47.6%) pREE equations were significantly different from mREE ( P <0.05). Harris-Benedict equation had the smallest limits of agreement, ranging from -14.3% to 25.8% (or -249 to 393 kcal/d). Mifflin St-Jeor was the most accurate equation (within 10% of mREE). The best performing equation (Mifflin St-Jeor) still over or under-estimated pREE in ∼37% of the patients. Conclusion A large variability in mREE versus pREE was observed in COVID-19 survivors. Even the most accurate equation (Mifflin St-Jeor) exhibited higher inaccuracies compared to mREE. We need to explore better methods to estimate energy requirements during the COVID-19 recovery period, until more accurate predictive equations are developed this population.
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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 | 0.007 | 0.190 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".