Hubungan Kadar HB dengan Perdarahan Postpartum di Rumah Sakit Umum Daerah Djasamen Saragih Tahun 2023
Bibliographic record
Abstract
According to the WHO (World Health Organization), every day in 2017 around 810 women died, at the end of the year reaching 295,000 people, of which 94% were in developing countries (WHO, 2019). The MDGs (Millennium Development Goals) which ended in 2015, were then continued with the development of the SDGs (Sustainability Development Goals) until 2030. The five biggest causes of maternal death in Indonesia in 2010-2016 were bleeding (30.3%), hypertension in pregnancy ( 27.1%), infection (7.3%), prolonged labor (1.8%), abortion (1.6%) and others (31.9%). This study aims to identify the relationship between HB levels and postpartum hemorrhage at the Djasamen Saragih Regional General Hospital in 2023.Type of quantitative research with a case control design. This study was conducted on a sample of 100 postpartum. To measure the characteristics of the respondents, a data collection form was used which was obtained through collecting data from the evaluation section at the Djasamen Saragih Regional General Hospital in 2023.Based on the statistical test using chi-square, the value of ρ = 0.001 (ρ <α, α = 0.05) is obtained, the strength of the relationship between the two variables is seen based on the contingency coefficient, which is 0.302, which means the strength of the relationship is moderate. Then the odds ratio (OR) results obtained OR = 0.215 [95% CI 0.087 – 0.532] which means that the range 0.087 – 0.532 does not exceed the value of 1, so postpartum mothers with low Hb levels during pregnancy have a greater chance of postpartum hemorrhage 0.215 than mothers postpartum Hb was normal during her pregnancy.There is a relationship between Hb levels and postpartum hemorrhage at the Djasamen Saragih Regional General Hospital in 2023
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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".