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Record W4400128146 · doi:10.21926/obm.icm.2402038

The Interplay of Nutrition, Exercise, and Dietary Intervention for Enhanced Performance of Athletes and General Well-Being of Non-Athletes: A Review

2024· review· en· W4400128146 on OpenAlexaff
Oghenerume Lucky Emakpor, Great Iruoghene Edo, Emad Yousif, Princess Oghenekeno Samuel, Agatha Ngukuran Jikah, Khalid Zainulabdeen, Athraa Abdulameer Mohammed, Winifred Ndudi, Susan Chinedu Nwachukwu, Ufuoma Ugbune, Joy Johnson Agbo, Irene Ebosereme Ainyanbhor, Huzaifa Umar, Helen Avuokerie Ekokotu, Ephraim Evi Alex Oghroro, Patrick Othuke Akpoghelie, Joseph Oghenewogaga Owheruo, Lauretta Dohwodakpo Ekpekpo, Priscillia Nkem Onyibe, Ufuoma Augustina Igbukuc, Endurance Fegor Isojec, Arthur Efeoghene Athan Essaghahk

Bibliographic record

VenueOBM Integrative and Complementary Medicine · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsGeorge Brown College
Fundersnot available
KeywordsAthletesSports nutritionPhysical therapyIntervention (counseling)MedicinePerformance enhancementPhysical medicine and rehabilitationNursing

Abstract

fetched live from OpenAlex

The optimal enhancement of athletic performance, recovery from exhaustion after exercise, and injury prevention are products of appropriate nutrition. Nutritional supplements that contain proteins, carbohydrates, vitamins, and minerals are frequently utilized in various sports to complement the recommended daily amounts. Several of these supplements have been identified to have physiological effects and, thus, are known to help enhance athletic performance and prevent injuries. Our review intends to show the interplay between nutrition, exercise, and dietary intervention on the physical performance of athletic individuals and their importance for the general well-being of non-athletes. Ergogenic aids that help enhance athletic performance are also discussed.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.877
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.018
GPT teacher head0.342
Teacher spread0.325 · 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 designOther design
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

Citations8
Published2024
Admission routes1
Has abstractyes

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