Haematological Indices and Obstetric Outcomes Among Pregnant Women with Preeclampsia at Iringa Regional Tanzania (14)
Bibliographic record
Abstract
Abstract Introduction Preeclampsia is a common problem in pregnancy after 20 weeks of gestation age. This is when the systolic blood pressure is 140 and above and diastolic blood pressure is above 90mmhg with proteinuria. Hematological system is affected and can result into low platelets, low hemoglobin level, high packed cells volume, and reduced Red blood cells, this can result in bad obstetric outcome. This study aimed to assess the hematological indices in Preeclampsia and their maternal outcomes. Objectives Assessment of Hematological indices and maternal outcomes among women with Preeclampsia. Material and methods The study was conducted at Iringa Regional referral hospital, it was cross sectionals study. The sample size was 100 participants. Data were collected by using a well structured questionnaire which has been tested, the information’s collected. Full blood count investigation done in a cimex 300 machine. Results During the study above 18years of age 44% had diastolic above 160mmhg, 44% had platelets below 150, 31% had high hematocrit and 59% had low hemoglobin levels. Among the respondents 78% had bad maternal outcomes. Those with gestation age below 37 weeks are statistically significant with bad maternal outcomes; the blood loss above 500mls was statistically significant with the bad maternal outcomes Conclusion The finding of this study show that the preeclampsia and severe preeclampsia can result into changes in hematological indices and this can result into adverse obstetric outcomes. The study will help to Improve better care of the women with preeclampsia and reduce hospital stay.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".