Current Trends and Research Hot Spots in Traumatic Birth: A Bibliometric Analysis
Bibliographic record
Abstract
The identification of traumatic birth is becoming a major global health concern. Evaluating the existing research can help shape future directions for traumatic birth studies. This study aimed to provide a comprehensive and up-to-date summary of research articles on traumatic birth. We performed a bibliometric analysis using the Science Citation Index Expanded of the Web of Science Core Collection database, covering the period from January 1, 1985, to June 30, 2023. A total of 1,568 original articles were found, indicating a significant increase in traumatic birth research. The United States was the most prolific country, followed by Australia and Canada. The University of Sydney, the University of Toronto, and the University of Pittsburgh were the top 3 institutions in terms of published documents. The terms "infants," "perceptions," and "birth injuries" had the highest burst strengths. MeSH Bibliographic Item Co-Occurrence Matrix Builder analysis identified six major research topics, with birth injuries and their prevention and control, as well as brachial plexus/shoulder injuries and surgery, being the most concentrated areas. While traumatic birth is not yet universally recognized and its scope remains under discussion, it is increasingly becoming a significant issue. Understanding the priorities and trends of research can guide future academic endeavors, highlighting areas that require further investigation and development.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.182 | 0.233 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".