PENGARUH PEMBERIAN GLISIN TERHADAP KADAR HEMATOKRIT REMAJA PUTRI DENGAN ANEMIA YANG MENDAPAT SUPLEMENTASI ZAT BESI
Bibliographic record
Abstract
Background: Nutritional anemia has bad effect to adolescent girls. Adolescent girls susceptible to nutritional anemia because of imbalancing between iron intake from food with increasing iron requirement. Anemic status determination can be shown by hematocrit level. Glycine, one of conditionally essential amino acids involved in hemoglobin biosynthesis, is predicted to enhance iron bioavailability that hematocrit level may be increased. This study had a purpose to prove that glycine addition on iron supplementation can affect the hematocrit level of anemic adolescent girls. Method: This true experimental study was Randomized Controlled Trial design. To the number of 20 adolescent girls with mild-moderate anemia were taken their blood speciment to measure first hematocrit value, then divided into two groups, control group (receive iron and placebo) and treatment group (receive iron and glycine). After 5 weeks treatment, they were examined again to measure the second hematocrit value. The data were processed by using SPSS 13.00 for windows, analyzed by using the Shapiro-Wilk test and Paired T-test. Results: The treatment group shows a statistically significance hematocrit level improvement, whereas the control group doesn’t show hematocrit level improvement. Conclusion: Glycine addition may raise anemic adolescent girls’s hematocrit level that receive iron supplementation. Key Words: glycine, adolescent girls, iron supplementation, hematocrit.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".