Trends, Topics, and Visualization Analysis of Global Scientific Production on Maternal Mortality from Postpartum Hemorrhage: A 5-year Bibliometric Analysis
Bibliographic record
Abstract
Abstract BACKGROUND: Maternal death generates a great impact on public health, and it is recognized that its main cause is postpartum hemorrhage (PPH). Therefore, the objective was to analyze the bibliometric profile of the world scientific production on maternal mortality due to PPH. MATERIALS AND METHODS: Bibliometric study that included original articles indexed in Scopus, identified by means of a search strategy that included MESH terms and logical operators. Bibliometric indicators were estimated with the SciVal tool, and the VOSwiever program was used for co-occurrence networks by key terms and co-authorship by country. RESULTS: There has been an increase in the number of publications in the past 5 years. Regarding co-occurrence, the most frequent terms were “postpartum hemorrhage” and “maternal mortality.” The United States and the United Kingdom are positioned as those with the highest density of publications; in addition, Australia evidences collaboration with Canada and South Korea. Publications with national collaboration were more frequent (36%). BMC Pregnancy and Childbirth is the most productive journal, although BJOG: An International Journal of Obstetrics and Gynecology has a higher normalized impact. The authors with the highest scientific output belong to an institution in the Netherlands. Vrije Universiteit Amsterdam and Leiden University are the most productive. CONCLUSION: Scientific activity on maternal mortality due to PPH is increasing and its main means of dissemination are high-impact journals. National collaboration was more frequent, with the active participation of authors and institutions from the Netherlands and the United States.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.027 | 0.082 |
| 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) | 0.000 | 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; both teacher heads agree on what is shown here.
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".