Total nutritional therapy: a nutrition education program for physicians.
Bibliographic record
Abstract
OBJECTIVE: Almost half of all hospitalized patients are malnourished with low physician awareness or implementation of nutrition support. To address this problem, a 2-day immersion course in clinical nutrition for physicians was developed by the Latin American Federation of Parenteral and Enteral Nutrition (FELANPE) with support from Abbott Laboratories. The goal of Total Nutritional Therapy (TNT) is to help physicians utilize this nutrition knowledge to increase their awareness of malnutrition and implementation of nutritional therapy. Since 1997, over 8,000 physicians have completed the TNT course in 16 Latin American countries. RESEARCH METHODS & PROCEDURES: During 1999 and 2000, 675 participants responded to a survey 6 months after having completed the TNT course to determine what impact the course had on the use of nutrition assessment, nutrition support teams, or nutrition consultations in their clinical practice, and if they had participated in any nutrition association or conferences. RESULTS: The majority of physicians who completed the survey increased their use of nutrition assessment and time dedicated to nutrition therapy, and increased the number of their patients placed on nutrition therapy. CONCLUSIONS: The TNT course has been shown to be an efficient model of clinical nutrition education for general physicians. The course should be considered as part of the training of medical residents.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 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".