Poster (Knowledge Generation) ID 1987820
Bibliographic record
Abstract
Background Anti-inflammatory diets have shown effective in reducing pro-inflammatory cytokines, and neuropathic pain and depression in individuals with spinal cord injury or disease (SCI/D). However, work to date has focused on community-dwelling individuals with SCI/D, and the diet’s efficacy in an inpatient population, and the feasibility of offering it in a hospital, are unknown. The inpatient setting may be ideal for introducing an anti-inflammatory diet, as immune-related health complications peak acutely after SCI, and forming new dietary habits may be easier in an inpatient setting. Thus, it is necessary to investigate the feasibility of an anti-inflammatory diet in the inpatient SCI/D setting. Objectives Phase 1: Understand the nutritional value of the current meal plans in selected inpatient SCI/D hospitals and their compliance with our anti-inflammatory diet. Phase 2: Understand the opinions that inpatients with SCI/D have regarding their currently offered meal choices and their readiness to learn about, and adopt, an anti-inflammatory diet. Phase 3: Understand the barriers and facilitators for implementing an anti-inflammatory diet in an inpatient setting from the perspective of hospital administrators. Proposed Design/Methods This study will take place in the SCI inpatient settings in two Ontario hospitals. Four to five inpatients from each site will be interviewed in Phase 1 and 2, and four to five Food Services administrators from each site will be interviewed in Phase 3. Menu plans, as well as individual food logs will be analyzed for nutritional value and compliance to an anti-inflammatory diet. Interviews will be subject to thematic analysis.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.941 | 0.738 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".