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Record W3096939602 · doi:10.46292/sci2603-197

A Primary Care Provider’s Guide to Diet and Nutrition After Spinal Cord Injury

2020· article· en· W3096939602 on OpenAlexaff
David R. Gater, Craig Bauman, Rachel E. Cowan

Bibliographic record

VenueTopics in Spinal Cord Injury Rehabilitation · 2020
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsCentre for Family MedicineRegional Municipality of Waterloo
Fundersnot available
KeywordsMedicineSpinal cord injuryCaloric theorySarcopeniaMicronutrientBalance (ability)AppetiteObesityIntensive care medicinePhysical therapyPhysiologySpinal cordInternal medicine

Abstract

fetched live from OpenAlex

Physiological changes that occur after spinal cord injury (SCI) are profound and affect almost every organ system in the human body. Energy balance is significantly altered due to motor paralysis, spasticity or flaccidity, neurogenic sarcopenia, neurogenic osteopenia, sympathetic nervous system disruption, and blunted anabolism. Energy expenditure is markedly reduced, whereas hypothalamic control of appetite and satiety is diminished, resulting in discordant energy intake. Ultimately, neurogenic obesity ensues as the result of a positive energy balance. Even though nutritional guidelines for persons with SCI have been available since 2009, the necessity for body composition assessment and total daily energy expenditure was insufficiently addressed such that most individuals with SCI continued in positive energy balance despite "adherence" to the guidelines. Macronutrients must be carefully assessed to optimize caloric intake, while micronutrient consumption may need to be supplemented in order to meet recommended daily allowances. Such a diet would emphasize foods with low caloric yet high nutrient density. This article reviews current literature regarding nutritional requirements for SCI and provides a straightforward plan for implementing more rigorous dietary interventions meant to address the obesity crisis in this especially vulnerable population.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.879

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.020
GPT teacher head0.337
Teacher spread0.317 · 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 designObservational
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

Citations8
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueTopics in Spinal Cord Injury RehabilitationSame topicCerebral Palsy and Movement DisordersFrench-language works237,207