Digital Scales of Web-Based Partograph in Detecting the Early Postpartum Bleeding
Bibliographic record
Abstract
INTRODUCTION: A simple graph partograph serves to record the information of the inpartu mothers during their 1st active phase of labor, to detect complications, to make an appropriate action in decision making, to prevent prolonged labor, postpartum hemorrhage, and sepsis. The aimof this research was to compare the use of digital scales of web-based partograph and the conventional partograph in detecting the early estimation of postpartum bleeding at the stage III and IV. MATERIALS & METHODS: The study used the Quasi-Experiment method.Thirty women in labor were chosen by using the purposive sampling technique. The web digital scale and the conventional digital scale were used to weigh the underpad of the postpartum at the stage III and IV. The Independent T-test and Mann-Whitney test were employed to analyze the data. RESULTS: The results of the study indicated that the digital scale of the WEB-based partograph was more rapid in estimating the postpartum hemorrhage at the stage III and IV comparing to the digital scale of conventional partograph. The statistical test of Mann-Whitney revealed the p-value > 0.05, which means that there was a difference in the speed but in case of the accuracy aspect of p-value > 0.05, it means that there was no difference. In case of the estimation of the blood amount at stage III and IV, the test value of the Independent T-test revealed the p-value of > 0.05, which means that there was no difference in the estimation of the postpartum hemorrhage. CONCLUSION: It was concluded that the digital scale of web-based partograph was faster in estimating postpartum hemorrhage at period III and IV comparing to the digital scales of conventional partograph and there were no differences in its accurateness and number of postpartum hemorrhage at the stage III and IV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".