A Bibliometric Analysis of Neonatal Pain Management Research From 2010 to 2022
Bibliographic record
Abstract
BACKGROUND: Research related to neonatal pain management has received increasing attention in recent years. Effective pain management contributes to the maintenance of the healthy physical and mental growth of the newborn. To better understand this research topic, we analyzed the current state of development in this field over the past thirteen years by bibliometric analysis and provide directions for future research. METHODS: Original articles were collected from the Web of Science Core Collection (WoSCC) between January 1, 2010, to December 31, 2022, the title and abstract clearly stating 'neonatal pain management' or its alternative search keywords. CiteSpace, VOSviewer, and the WoS analysis tool were used to analyze and present the data. RESULTS: A total of 967 articles met the inclusion criteria. Significant growth of the number of publications increased roughly fourfold from 2010 to 2022. Overall, the United States and Canada were the highest contributors to neonatal pain management research. Weak cooperation was observed in international research (developing and developed countries) and cross-institutional cooperation. Neonatal pain-related research was the most common focus. Pain education and interventions for parents and medical personnel have also received increasing attention recently. CONCLUSION: The current study revealed that research in terms of publications on neonatal pain management has rapidly increased for more than the past ten years. Developed countries, especially the United States and Canada, were more concerned with this topic than developing countries. More international research and cross-institutional cooperation are required to promote the development of neonatal pain medicine in the future.
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 | 0.012 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.218 | 0.298 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".