MétaCan
Menu
Back to cohort
Record W3155466007 · doi:10.3389/fnut.2021.652441

Protein Considerations for Athletes With a Spinal Cord Injury

2021· review· en· W3155466007 on OpenAlexaff
Joëlle Leonie Flueck, Jill A. Parnell

Bibliographic record

VenueFrontiers in Nutrition · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsMount Royal University
Fundersnot available
KeywordsAthletesContext (archaeology)Spinal cord injuryNarrative reviewMedicinePhysical medicine and rehabilitationAffect (linguistics)Physical therapyPsychologyIntensive care medicineSpinal cordPsychiatryBiology

Abstract

fetched live from OpenAlex

Athlete participation in the Paralympic games is steadily increasing; prompting research focused on the unique needs of this population. While the Paralympic Games includes a diversity of athletes, athletes with a spinal cord injury (PARA-SCI) represent a subgroup that requires specialized recommendations. Nutritional guidelines designed to optimize performance, in the context of the neurological impairments, are required. This narrative review summarizes the current literature regarding the importance of dietary protein for optimal health and performance. Factors with the potential to affect protein needs in PARA-SCI including loss of active muscle mass, reduced energy expenditure, and secondary complications are examined in detail. Furthermore, we analyze protein intakes in PARA-SCI from the available research to provide context around current practices and trends. In conclusion, we make the case that protein recommendations for able-bodied athletes may not be directly transferable to PARA-SCI. Consequently, PARA-SCI need their own guidelines to maximize performance and ensure long-term health.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.903
Threshold uncertainty score1.000

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 head0.030
GPT teacher head0.313
Teacher spread0.283 · 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

Citations19
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in NutritionSame topicMuscle metabolism and nutritionFrench-language works237,207