Pengaruh Pemberian Green Bean Juice Terhadap Peningkatan Hemoglobin Pada Ibu Hamil Dengan Anemia Di PMB LT Jatirahayu
Bibliographic record
Abstract
Introduction: Red blood cells contain an iron protein called Hemoglobin which binds and distributes oxygen to the body's cells. In anemia conditions, the number of red blood cells and hemoglobin is reduced so that oxygen is not supplied properly and the patient complains of weakness and paleness. During pregnancy, women sometimes experience deficiencies in micronutrients such as zinc. So, grams need to be careful and consult with a doctor about what the body needs to prevent the fetus from developing. The content of zinc and iron in green beans also has benefits for pregnant women or pregnant women. Not only these two ingredients can also reduce the risk of premature babies. Methods: The design of this study used an experimental method (pre-test) with a one-group pre-test-post-test design. Results: The results of the T-test showed that the t-count value was-7617 with a p-value (0,000) less than 0,05. Discussion: Giving green beat extract has an effect in increasing Hemoglobin levels in pregnant women with anemia at PMB LT Jatirahayu, Pondok Melati District, Bekasi Regency. Socialization or campaigns about giving mung bean extract as a treatment in increasing Hemoglobin levels in pregnant women with anemia need to be carried out by PMB in order to increase Hemoglobin levels.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".