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Record W3134645214 · doi:10.29252/aassjournal.976

Physical Activity and Natural Products and Minerals in the SARS-CoV-2 Pandemic: An Update

2021· article· en· W3134645214 on OpenAlexaff
Ayoub Saeidi, Seyed Morteza Tayebi, Oam To-aj, Niloofar Karimi, Sahar Kamankesh, Saber Niazi, Abedin Khosravi, Mitra Khademosharie, Mohammad Soltani, Kelly E. Johnson, Harunor Rashid, Ismail Laher, Anthony C. Hackney, Hassane Zouhal

Bibliographic record

VenueAnnals of Applied Sport Science · 2021
Typearticle
Languageen
FieldNursing
TopicVitamin C and Antioxidants Research
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Arthritis and Musculoskeletal and Skin Diseases
KeywordsPandemicImmune systemCoronavirusPublic healthMedicineDiseaseObesityInfectious disease (medical specialty)Coronavirus disease 2019 (COVID-19)ImmunologyEnvironmental healthImmune statusIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Coronavirus-disease 19 (COVID-19) has rapidly become a global public health issue, and there is a desperate need for strategies of prevention, reduction, and treatment to halt the epidemic. The coronavirus affects the immune system, and individuals with a compromised immune system, such as those with diabetes, hypertension, obesity, are more susceptible to this virus. Lifestyle-related variables such as physical activity and nutritional supplements can decrease inflammatory markers, increase anti-inflammatory and antioxidant status, and improve the immune system. Lifesty-lerelated variables play preventive roles against various infectious diseases including COVID-19. This review highlights the effects of physical activity and nutrients supplements on the immune system and their possible benefits in combating the harms caused by infection with the COVID-19 virus.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.392
Teacher spread0.315 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations4
Published2021
Admission routes1
Has abstractyes

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