Web-Based Partograph on Early Detection of Emergency Cases and Referral Processes
Bibliographic record
Abstract
INTRODUCTION: The partograph is designed as a tool to monitor a woman’s progress of labor. If it is used appropriately, the partograph can be used as a means of prevention and an early warning system to the need for further action such as caesarian section. The used of partograph is able to lower the percentage of Maternal Mortality Rate (MMR) and Infant Mortality Rate (IMR). This study aimed to determine the comparison between word electric browser (WEB)-based partograph and the conventional partograph. MATERIALS & METHODS: This study aimed to compare between the use of WEB-based patorgaph and conventional patograph. To gain the data, the researcher adopted quasi experimental method. Using purposive sampling technique, 30 women in labour were participated in this study. The data were analyzed by using the Independent T- test and Mann-Whitney test. RESULTS: The result of the study showed that the utilization of WEB-based partograph is faster in recording the contraction, oxytocin, and the process of giving birth than the utilization of conventional partograph. Furtehrmore, promptness of WEB-based partograph in early detection has p-value 0.000 (<0.05) and p-value in emergency detection is 0.014 (<0.05) which means that there was differences between the use of WEB-based partograph and conventional partograph. Meanwhile, p-value of referral process is 1.000 (>0.05) which means that there was no difference in using both WEB-based partograph and conventional partograph. CONCLUSION: This means that using the monitoring of the WEB-based partograph or the conventional partograph showed differences. The utilization of WEB-based partograph is better than conventional partograph since it can be used as a means to monitor the progress of labour.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".