IMPACT OF MOBILE FOLLOW UP PROTOCOL OF CARE ON PREGNANCY OUTCOMES AMONG PREECLAMPTIC WOMEN: A RANDOMIZED CONTROLLED TRIAL (RCT)
Bibliographic record
Abstract
Abstract Background: Hypertensive disorders of pregnancy (HDP) which includes preeclampsia (PE) constitute one of the leading causes of maternal and perinatal mortality worldwide, the integration of artificial intelligence (AI) in monitoring preeclampsia has revolutionized maternal healthcare by enabling early detection, personalized management, and improved outcomes. The aim: was to evaluate the impact of mobile follow-up protocol of care on the pregnancy outcomes among preeclamptic women. Design: A randomized controlled trial (RCTs) design was adopted for this study. Sample: Purposive sample of (100) women with mild preeclampsia, were randomly recruited and randomly assigned in two group, 50 women in the study group who received routine hospital care and mobile follow-up protocol of care utilizing motab3a mobile app, and 50 women in the control group who received routine hospital care only. Tools: three tools were used for data collection; 1) Structured interview schedule, 2) Initial assessment and maternal /fetal follow up tool and 3) Maternal, fetal and neonatal outcomes evaluation tool and Motab3a mobile application and instructional booklets as a supportive material. Results: the results of current study revealed that there are statistical significance differences between groups regarding systolic and diastolic blood pressure, weight gain and edema degree (p=0.001), the results showed that women who received follow-up and monitoring through Motab3a mobile app showed better pregnancy outcomes as compared to control group. As; decrease occurrence of severe preeclampsia, APH, hospital admission, preterm delivery, PPH. Low rate of fetal complications as; oligohydromious, IUGR as compared to the control group, and NICU admission, neonatal weight and gestational age among motab3a app group had lower rate as compared to the control group with (p<0.05). Conclusion: women with mild preeclampsia who utilize follow-up protocol of care through motab3a mobile application is associated with better pregnancy outcomes. Recommendations: integrate a new technology in monitoring women who at risk during pregnancy and follow up protocol of care should be included in health care system for all risky groups and to be a part from antenatal care.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".