The Philosophy of Evidence-Based Principles and Practice in Nutrition
Bibliographic record
Abstract
The practice of evidence-based nutrition involves using the best available nutrition evidence, together with clinical experience, to conscientiously work with patients' values and preferences to help them prevent (sometimes), resolve (sometimes), or cope with (often) problems related to their physical, mental, and social health. This article outlines the 3 fundamental principles of evidence-based practice as applied to the field of clinical nutrition. First, optimal clinical decision making requires awareness of the best available evidence, which ideally will come from unbiased systematic summaries of that evidence. Second, evidence-based nutrition provides guidance on how to decide which evidence is more or less trustworthy-that is, how certain can we be of our patients' prognosis, diagnosis, or of our therapeutic options? Third, evidence alone is never sufficient to make a clinical decision. Decision makers must always trade off the benefits with the risks, burden, and costs associated with alternative management strategies, and, in so doing, consider their patients' unique predicament, including their values and preferences.
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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 | 0.185 | 0.226 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.003 | 0.037 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.018 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".