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Impact of micronutrients on respiratory infections

2011· review· en· W2099023997 on OpenAlexfundno aff
Christopher E. Taylor, Carlos A. Camargo

Bibliographic record

VenueNutrition Reviews · 2011
Typereview
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsnot available
FundersUniversity of Colorado DenverUniversity of North Carolina at Chapel HillUniversity of South CarolinaUniversity of California, Los AngelesNational Institute of Standards and TechnologyBrigham and Women's HospitalUniversity of TorontoPublic Health AgencyJohns Hopkins UniversityCreighton UniversityQueen Mary University of LondonUniversity College Cork
KeywordsMicronutrientImmune systemObservational studyVitamin D and neurologyImmunologyBiologyMedicineRespiratory tract infectionsPhysiologyIntensive care medicineInternal medicineRespiratory systemPathology

Abstract

fetched live from OpenAlex

Several studies have documented the impact of vitamin D and other micronutrients on host responses to upper and lower respiratory tract infections, such as influenza and tuberculosis. These studies include observational as well as micronutrient intervention studies. Other studies have been conducted to understand the mechanisms by which micronutrients alter immune responses. However, critical information gaps and challenges remain. An immediate need exists for randomized controlled trials of vitamin D supplementation in high-risk populations, such as infants, children, and patients with immunocompromised health. Other important areas of research include vitamin D genetics, the impact of other micronutrient deficiencies on innate and adaptive immunity, the 25(OH)D threshold for insufficiency, the need for valuable reliable markers, standardization of assays to detect 25(OH)D, novel functional markers beyond serum 25(OH)D, and further development of in vitro and animal models that could be useful for preclinical studies. Lastly, a new systems biology approach is needed to address the complexity of micronutrient effects and regulation.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.766
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.218
GPT teacher head0.478
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations49
Published2011
Admission routes1
Has abstractyes

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