Serum albumin as a measure of inflammation or malnutrition in inflammatory bowel disease: a cross sectional study
Bibliographic record
Abstract
Albumin may be both a marker of malnutrition and a marker of inflammation in various disease states but there has been very little study of the precise etiology of hypoalbuminemia in patients with Crohn’s disease (CD). Malnutrition, a common complication of active CD, can lead to hypoalbuminemia. Inflammation can also lead to low albumin levels, and previous literature in other inflammatory diseases has suggested that inflammation may be more likely than malnutrition to be a primary driver of hypoalbuminemia. This study was a single centre cross sectional study of patients with Crohn’s disease in St. John’s, NL. The main objectives were to examine the association between serum albumin and both inflammation and malnutrition in patients with CD and to determine if serum albumin is an appropriate indicator of one or both of these processes in patients with CD. A total of 45 patients with Crohn’s disease were enrolled in the study. Serum albumin was compared with the subjective global assessment (SGA) for nutritional status, the Crohn’s disease activity index (CDAI), and CReactive Protein (CRP), a marker of inflammation. In our study hypoalbuminemia was independently associated with both malnutrition and inflammation in patients with CD but was most profound in subjects with both malnutrition and active inflammation.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".