A235 SYSTEMATIC REVIEW AND META-ANALYSIS: COMPARING OF ESTIMATED ENERGY REQUIREMENTS IN CIRRHOTIC PATIENTS USING INDIRECT CALORIMETRY VERSUS PREDICTION EQUATIONS
Bibliographic record
Abstract
Malnutrition is common in cirrhosis and an independent predictor of mortality. Dietary recommendation is the mainstay of therapy. Most dietitians utilize predictive equations to estimate resting energy expenditure (REE) and target energy need as these are more time-efficient than gold-standard indirect calorimetry. However, predictive equations are associated with over- and under-estimation of energy requirements. As accurate nutrition prescriptions are important in cirrhosis patient care, our aim was to compare the estimated energy requirements using indirect calorimetry measurements (measured REE, mREE) versus prediction equations (predicted REE, pREE). We included full-text English language studies on adults with cirrhosis comparing pREE versus mREE. Excluded studies had >20% of patients with hepatocellular carcinoma. A DerSimonian-Laird random-effects meta-analysis was used to pool the mean differences across studies. A total of 20 studies (2 separately reporting data in men and women) comprising 1883 patients (1991 to 2016) fulfilled selection criteria and were analyzed. The Harris-Benedict equation was used to estimate the pREE in 15 studies (75%). A total of 14 studies underestimated caloric requirements using the predictive equations. The percentage difference between the mREE and the pREE, ranged from -12.0% (overestimation) to +22.2% (underestimation) kilocalories per day (kcal/d). When pooled, the mean difference across studies was an underestimate of 75.9 (95% CI: 12.83–138.96) kcal/d. The pooled analysis was associated with significant heterogeneity (I2=93%, Fig 1). It is important to recognize that predictive equations have a wide margin of error and are more likely to underestimate rather than overestimate caloric requirements of cirrhotic patients. Our results highlight the need to accurately define the subgroup of patients at risk for underestimation of caloric requirements and have them complete indirect calorimetry assessment. Future work will analyze individual patient data evaluating the impact of liver disease severity (e.g., ascites, obesity, and hospitalization) to identify this at-risk subgroup. Fig 1. Forest plot of comparison: measured Resting Energy Expenditure (mREE) vs. predicted REE (pREE). Abbreviations in parenthesis show the predictive equations employed by each study (HB: Harris-Benedict, FFM & FFM in reg.: using the individual fat free mass in the linear regression equation derived from cirrhotic or control group, sex reg.: using gender specific regression equations derived from healthy population) None
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.042 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".