Malnutrition: laboratory markers vs nutritional assessment
Bibliographic record
Abstract
Malnutrition is an independent risk factor for patient morbidity and mortality and is associated with increased healthcare-related costs. However, a major dilemma exists due to lack of a unified definition for the term. Furthermore, there are no standard methods for screening and diagnosing patients with malnutrition, leading to confusion and varying practices among physicians across the world. The role of inflammation as a risk factor for malnutrition has also been recently recognized. Historically, serum proteins such as albumin and prealbumin (PAB) have been widely used by physicians to determine patient nutritional status. However, recent focus has been on an appropriate nutrition-focused physical examination (NFPE) for diagnosing malnutrition. The current consensus is that laboratory markers are not reliable by themselves but could be used as a complement to a thorough physical examination. Future studies are needed to identify serum biomarkers in order to diagnose malnutrition unaffected by inflammatory states and have the advantage of being noninvasive and relatively cost-effective. However, a thorough NFPE has an unprecedented role in diagnosing malnutrition.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".