NUTRI TIONAL HEALTH PROBLEMS OF TRANSGENDERS AND THEIR MEDICAL NUTRITION THERAPHY
Bibliographic record
Abstract
Background: Nutritional Health Status Assessment was performed for Transgender / Eunuchs to find out the current health issues along with Nutritional Deficiencies they werefacing.Methodology: Currently this cross-sectional study was carried out in Fountain House Sargodha under the Institute of Food Sciences and Nutrition, University of Sargodha. Total participants (150) who belongs to third gender community were selected for Nutritional Health Status Assessment. Eunuchs / transgender who were agreed for NHS were included in study and those who were having any serious health problems were excluded. The purpose was as Eunuchs experience unique health disparities but are the subject of life focused health research. Demographic Performa with dietary guidelines was used to maintain the record for calculations and heath assessing equipment's were used to check Height, weight, Body Fats, Water Composition, Muscle mass, Bone mass, AMR, Pulse rate, Oxygen saturation rate, Blood Pressure.Results: Results using descriptive statistics showed that average mean height among 90% of Eunuch's were having height range within 164.64cm, Body fats were 31.38% higher than normal. Water Composition was very low (50.025%) should be more than 65%., Whereas muscle mass was in range (36.39%).Conclusions: it was concluded that 90%- 95% trans genders were facing many Nutritional deficiencies and were categorized as being Obese.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".