A Primary Care Provider’s Guide to Diet and Nutrition After Spinal Cord Injury
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.109 | 0.042 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".