Micronutrient Deficiency and Its Potential Role in Delirium Onset in Older Adults: A Systematic Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: One of the pathogenetic hypotheses of delirium is the "neuroinflammatory theory" with consequent neurotoxicity of brain connectivity networks. Micronutrients may play a significant role in the prevention of neuroinflammation. This systematic review addresses the role of micronutrients in the development of delirium in older populations. METHODS: The EBSCO, Cochrane, PubMed, and Web of Science databases were searched for articles on delirium and micronutrients. The methodological quality of the studies included in the review was evaluated with the Newcastle-Ottawa Scales for observational studies and for case-control studies. RESULTS: 1326 papers were identified from the searches, 7 of which met the inclusion criteria (see section 2.3). All the papers included were written in English. Delirium was predominantly secondary to post-operative dysfunction or acute medical conditions. By altering the production of neurotransmitters resulting in an imbalance, and by reducing their immunomodulatory role with a consequent increase in inflammatory oxidative stress, micronutrient deficiency seems to be associated with an increased incidence of delirium. CONCLUSIONS: This review supports the existence of an association between micronutrient deficiency (i.e. cobalamin, thiamine, and vitamin D) and an increased incidence of delirium, with a greater prevalence in hospitalized patients.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".