Clinical nutrition knowledge of gastroenterology fellows: is there anything omitted?
Bibliographic record
Abstract
Despite the increased emphasis on chronic non-communicable diseases, there are notable deficits about nutrition education in many medicine training programs particularly gastroenterology fellowship programs. In the present cross-sectional study, we examined the nutritional knowledge related to clinical nutrition among Iranian gastroenterology fellows. Thirty-six gastroenterology fellows currently enrolled in a gastroenterology fellowship program completed a questionnaire, including two sections. The first of which assessed the gastroenterology fellows experience about nutrition training, nutrition management of patients with gastrointestinal (GI) disorders and evaluating perceived nutrition education needs. The second section consisted of multiple choice questions that assessed nutritional knowledge. A total of 32 gastroenterology fellows completed the first section. The majority of gastroenterology fellows failed to partake in any nutrition education during their fellowship training particularly for inpatients despite the availability to participate in the nutrition training especially for the purpose of nutrition support. Mean correct response rates for the second section was 38%. The highest mean score was seen in nutrition assessment (48.1%), followed by scores of 40.5% in nutrition support, 37.0% nutrition in GI disease, and 25.0% in micro and macronutrients. Iranian gastroenterology fellows have serious deficits in their nutrition knowledge. This study paves the way for the development of an education program to improve nutritional knowledge of gastroenterology fellows.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".